WO2025201524A1

WO2025201524A1 - Method, apparatus, and medium for point cloud coding

Info

Publication number: WO2025201524A1
Application number: PCT/CN2025/085819
Authority: WO
Inventors: Yingzhan XU; Kai Zhang; Li Zhang
Original assignee: Douyin Vision Co Ltd; ByteDance Inc
Current assignee: Douyin Vision Co Ltd; ByteDance Inc
Priority date: 2024-03-29
Filing date: 2025-03-28
Publication date: 2025-10-02
Anticipated expiration: 2026-09-29

Abstract

Embodiments of the present disclosure provide a solution for point cloud coding. A method for point cloud coding is proposed. The method comprises: determining, for a conversion between a point cloud (PC) sample of a point cloud sequence and a bitstream of the point cloud sequence, that a target process is performed for a set of attributes in a plurality of attributes of a processing unit associated with the PC sample;and performing the conversion based on the set of processed attributes.

Description

METHOD, APPARATUS, AND MEDIUM FOR POINT CLOUD CODING

FIELDS

Embodiments of the present disclosure relates generally to point cloud coding techniques, and more particularly, to improvements to multiple attribute compression for point cloud compression.

BACKGROUND

A point cloud is a collection of individual data points in a three-dimensional (3D) plane with each point having a set coordinate on the X, Y, and Z axes. Thus, a point cloud may be used to represent the physical content of the three-dimensional space. Point clouds have shown to be a promising way to represent 3D visual data for a wide range of immersive applications, from augmented reality to autonomous cars.

Point cloud coding standards have evolved primarily through the development of the well-known MPEG organization. MPEG, short for Moving Picture Experts Group, is one of the main standardization groups dealing with multimedia. In 2017, the MPEG 3D Graphics Coding group (3DG) published a call for proposals (CFP) document to start to develop point cloud coding standard. The final standard will consist in two classes of solutions. Video-based Point Cloud Compression (V-PCC or VPCC) is appropriate for point sets with a relatively uniform distribution of points. Geometry-based Point Cloud Compression (G-PCC or GPCC) is appropriate for more sparse distributions. However, coding efficiency of conventional point cloud coding techniques is generally expected to be further improved.

SUMMARY

Embodiments of the present disclosure provide a solution for point cloud coding.

In a first aspect, a method for point cloud coding is proposed. The method comprises: determining, for a conversion between a point cloud (PC) sample of a point cloud sequence and a bitstream of the point cloud sequence, that a target process is performed for a set of attributes in a plurality of attributes of a processing unit associated with the PC sample; and performing the conversion based on the set of processed attributes. Compared with the conventional solution, the method in accordance with the first aspect of the present disclosure can advantageously perform the target process on the set of attributes. In this way, the coding efficiency of point cloud coding can be improved.

In a second aspect, another method for point cloud coding is proposed. The method comprises: performing, for a conversion between a point cloud (PC) sample of a point cloud sequence and a bitstream of the point cloud sequence, a process for an attribute coding for a processing unit associated with the PC sample, wherein the process comprises at least one of: a generation of a level of a detail, a process of predictor search, a preprocessing of a geometry coordinate, a generation of a reference processing unit, or a coordinate preprocessing for a region-adaptive hierarchical transform (RAHT) ; and performing the conversion based on the processed processing unit. Compared with the conventional solution, the method in accordance with the second aspect of the present disclosure can advantageously make use of the process for the attribute coding. In this way, the coding efficiency of point cloud coding can be improved.

In a third aspect, another method for point cloud coding is proposed. The method comprises: generating, for a conversion between a point cloud (PC) sample of a point cloud sequence and a bitstream of the point cloud sequence, a reconstructed processing unit in a first processing unit level associated with the PC sample by adding a reconstructed processing unit in a second processing unit level, wherein attributes of a processing unit in the second processing unit level are reconstructed; and performing the conversion based on the reconstructed processing unit in the first processing unit level. Compared with the conventional solution, the method in accordance with the third aspect of the present disclosure can advantageously make use of a reconstructed processing unit in a processing unit level to generate a reconstructed processing unit in another processing unit level. In this way, the coding efficiency of point cloud coding can be improved.

In a fourth aspect, an apparatus for point cloud coding is proposed. The apparatus comprises a processor and a non-transitory memory with instructions thereon. The instructions upon execution by the processor, cause the processor to perform a method in accordance with the first, second, or third aspect of the present disclosure.

In a fifth aspect, a non-transitory computer-readable storage medium is proposed. The non-transitory computer-readable storage medium stores instructions that cause a processor to perform a method in accordance with the first, second, or third aspect of the present disclosure.

In a sixth aspect, another non-transitory computer-readable recording medium is proposed. The non-transitory computer-readable recording medium stores a bitstream of a point cloud sequence which is generated by a method performed by a point cloud processing apparatus. The method comprises: determining that a target process is performed for a set of attributes in a plurality of attributes of a processing unit associated with a point cloud (PC) sample of the point cloud sequence; and generating the bitstream based on the set of processed attributes.

In a seventh aspect, another non-transitory computer-readable recording medium is proposed. The non-transitory computer-readable recording medium stores a bitstream of a point cloud sequence which is generated by a method performed by a point cloud processing apparatus. The method comprises: performing a process for an attribute coding for a processing unit associated with a point cloud (PC) sample of the point cloud sequence, wherein the process comprises at least one of: a generation of a level of a detail, a process of predictor search, a preprocessing of a geometry coordinate, a generation of a reference processing unit, or a coordinate preprocessing for a region-adaptive hierarchical transform (RAHT) ; and generating the bitstream based on the processed processing unit.

In an eighth aspect, another non-transitory computer-readable recording medium is proposed. The non-transitory computer-readable recording medium stores a bitstream of a point cloud sequence which is generated by a method performed by a point cloud processing apparatus. The method comprises: generating a reconstructed processing unit in a first processing unit level associated with a point cloud (PC) sample of the point cloud sequence by adding a reconstructed processing unit in a second processing unit level, wherein attributes of a processing unit in the second processing unit level are reconstructed; and generating the bitstream based on the reconstructed processing unit in the first processing unit level.

In a ninth aspect, a method for storing a bitstream of a point cloud sequence is proposed. The method comprises: determining that a target process is performed for a set of attributes in a plurality of attributes of a processing unit associated with a point cloud (PC) sample of the point cloud sequence; generating the bitstream based on the set of processed attributes; and storing the bitstream in a non-transitory computer-readable recording medium.

In a tenth aspect, a method for storing a bitstream of a point cloud sequence is proposed. The method comprises: performing a process for an attribute coding for a processing unit associated with a point cloud (PC) sample of the point cloud sequence, wherein the process comprises at least one of: a generation of a level of a detail, a process of predictor search, a preprocessing of a geometry coordinate, a generation of a reference processing unit, or a coordinate preprocessing for a region-adaptive hierarchical transform (RAHT) ; generating the bitstream based on the processed processing unit; and storing the bitstream in a non-transitory computer-readable recording medium.

In an eleventh aspect, a method for storing a bitstream of a point cloud sequence is proposed. The method comprises: generating a reconstructed processing unit in a first processing unit level associated with a point cloud (PC) sample of the point cloud sequence by adding a reconstructed processing unit in a second processing unit level, wherein attributes of a processing unit in the second processing unit level are reconstructed; generating the bitstream based on the reconstructed processing unit in the first processing unit level; and storing the bitstream in a non-transitory computer-readable recording medium.

This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

Through the following detailed description with reference to the accompanying drawings, the above and other objectives, features, and advantages of example embodiments of the present disclosure will become more apparent. In the example embodiments of the present disclosure, the same reference numerals usually refer to the same components.

Fig. 1 illustrates a block diagram that illustrates an example point cloud coding system, in accordance with some embodiments of the present disclosure;

Fig. 2 illustrates a block diagram that illustrates an example of a GPCC encoder, in accordance with some embodiments of the present disclosure;

Fig. 3 illustrates a block diagram that illustrates an example of a GPCC decoder, in accordance with some embodiments of the present disclosure;

Fig. 4 illustrates a flowchart of a method for point cloud coding in accordance with embodiments of the present disclosure;

Fig. 5 illustrates a flowchart of a method for point cloud coding in accordance with embodiments of the present disclosure;

Fig. 6 illustrates a flowchart of a method for point cloud coding in accordance with embodiments of the present disclosure;

Fig. 7 illustrates a block diagram of a computing device in which various embodiments of the present disclosure can be implemented.

Throughout the drawings, the same or similar reference numerals usually refer to the same or similar elements.

DETAILED DESCRIPTION

Principle of the present disclosure will now be described with reference to some embodiments. It is to be understood that these embodiments are described only for the purpose of illustration and help those skilled in the art to understand and implement the present disclosure, without suggesting any limitation as to the scope of the disclosure. The disclosure described herein can be implemented in various manners other than the ones described below.

In the following description and claims, unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skills in the art to which this disclosure belongs.

References in the present disclosure to “one embodiment, ” “an embodiment, ” “an example embodiment, ” and the like indicate that the embodiment described may include a particular feature, structure, or characteristic, but it is not necessary that every embodiment includes the particular feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment. Further, when a particular feature, structure, or characteristic is described in connection with an example embodiment, it is submitted that it is within the knowledge of one skilled in the art to affect such feature, structure, or characteristic in connection with other embodiments whether or not explicitly described.

It shall be understood that although the terms “first” and “second” etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first element could be termed a second element, and similarly, a second element could be termed a first element, without departing from the scope of example embodiments. As used herein, the term “and/or” includes any and all combinations of one or more of the listed terms.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a” , “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” , “comprising” , “has” , “having” , “includes” and/or “including” , when used herein, specify the presence of stated features, elements, and/or components etc., but do not preclude the presence or addition of one or more other features, elements, components and/or combinations thereof.
Example Environment

Fig. 1 is a block diagram that illustrates an example point cloud coding system 100 that may utilize the techniques of the present disclosure. As shown, the point cloud coding system 100 may include a source device 110 and a destination device 120. The source device 110 can be also referred to as a point cloud encoding device, and the destination device 120 can be also referred to as a point cloud decoding device. In operation, the source device 110 can be configured to generate encoded point cloud data and the destination device 120 can be configured to decode the encoded point cloud data generated by the source device 110. The techniques of this disclosure are generally directed to coding (encoding and/or decoding) point cloud data, i.e., to support point cloud compression. The coding may be effective in compressing and/or decompressing point cloud data.

Source device 100 and destination device 120 may comprise any of a wide range of devices, including desktop computers, notebook (i.e., laptop) computers, tablet computers, set-top boxes, telephone handsets such as smartphones and mobile phones, televisions, cameras, display devices, digital media players, video gaming consoles, video streaming devices, vehicles (e.g., terrestrial or marine vehicles, spacecraft, aircraft, etc. ) , robots, LIDAR devices, satellites, extended reality devices, or the like. In some cases, source device 100 and destination device 120 may be equipped for wireless communication.

The source device 100 may include a data source 112, a memory 114, a GPCC encoder 116, and an input/output (I/O) interface 118. The destination device 120 may include an input/output (I/O) interface 128, a GPCC decoder 126, a memory 124, and a data consumer 122. In accordance with this disclosure, GPCC encoder 116 of source device 100 and GPCC decoder 126 of destination device 120 may be configured to apply the techniques of this disclosure related to point cloud coding. Thus, source device 100 represents an example of an encoding device, while destination device 120 represents an example of a decoding device. In other examples, source device 100 and destination device 120 may include other components or arrangements. For example, source device 100 may receive data (e.g., point cloud data) from an internal or external source. Likewise, destination device 120 may interface with an external data consumer, rather than include a data consumer in the same device.

In general, data source 112 represents a source of point cloud data (i.e., raw, unencoded point cloud data) and may provide a sequential series of “frames” of the point cloud data to GPCC encoder 116, which encodes point cloud data for the frames. In some examples, data source 112 generates the point cloud data. Data source 112 of source device 100 may include a point cloud capture device, such as any of a variety of cameras or sensors, e.g., one or more video cameras, an archive containing previously captured point cloud data, a 3D scanner or a light detection and ranging (LIDAR) device, and/or a data feed interface to receive point cloud data from a data content provider. Thus, in some examples, data source 112 may generate the point cloud data based on signals from a LIDAR apparatus. Alternatively or additionally, point cloud data may be computer-generated from scanner, camera, sensor or other data. For example, data source 112 may generate the point cloud data, or produce a combination of live point cloud data, archived point cloud data, and computer-generated point cloud data. In each case, GPCC encoder 116 encodes the captured, pre-captured, or computer-generated point cloud data. GPCC encoder 116 may rearrange frames of the point cloud data from the received order (sometimes referred to as “display order” ) into a coding order for coding. GPCC encoder 116 may generate one or more bitstreams including encoded point cloud data. Source device 100 may then output the encoded point cloud data via I/O interface 118 for reception and/or retrieval by, e.g., I/O interface 128 of destination device 120. The encoded point cloud data may be transmitted directly to destination device 120 via the I/O interface 118 through the network 130A. The encoded point cloud data may also be stored onto a storage medium/server 130B for access by destination device 120.

Memory 114 of source device 100 and memory 124 of destination device 120 may represent general purpose memories. In some examples, memory 114 and memory 124 may store raw point cloud data, e.g., raw point cloud data from data source 112 and raw, decoded point cloud data from GPCC decoder 126. Additionally or alternatively, memory 114 and memory 124 may store software instructions executable by, e.g., GPCC encoder 116 and GPCC decoder 126, respectively. Although memory 114 and memory 124 are shown separately from GPCC encoder 116 and GPCC decoder 126 in this example, it should be understood that GPCC encoder 116 and GPCC decoder 126 may also include internal memories for functionally similar or equivalent purposes. Furthermore, memory 114 and memory 124 may store encoded point cloud data, e.g., output from GPCC encoder 116 and input to GPCC decoder 126. In some examples, portions of memory 114 and memory 124 may be allocated as one or more buffers, e.g., to store raw, decoded, and/or encoded point cloud data. For instance, memory 114 and memory 124 may store point cloud data.

I/O interface 118 and I/O interface 128 may represent wireless transmitters/receivers, modems, wired networking components (e.g., Ethernet cards) , wireless communication components that operate according to any of a variety of IEEE 802.11 standards, or other physical components. In examples where I/O interface 118 and I/O interface 128 comprise wireless components, I/O interface 118 and I/O interface 128 may be configured to transfer data, such as encoded point cloud data, according to a cellular communication standard, such as 4G, 4G-LTE (Long-Term Evolution) , LTE Advanced, 5G, or the like. In some examples where I/O interface 118 comprises a wireless transmitter, I/O interface 118 and I/O interface 128 may be configured to transfer data, such as encoded point cloud data, according to other wireless standards, such as an IEEE 802.11 specification. In some examples, source device 100 and/or destination device 120 may include respective system-on-a-chip (SoC) devices. For example, source device 100 may include an SoC device to perform the functionality attributed to GPCC encoder 116 and/or I/O interface 118, and destination device 120 may include an SoC device to perform the functionality attributed to GPCC decoder 126 and/or I/O interface 128.

The techniques of this disclosure may be applied to encoding and decoding in support of any of a variety of applications, such as communication between autonomous vehicles, communication between scanners, cameras, sensors and processing devices such as local or remote servers, geographic mapping, or other applications.

I/O interface 128 of destination device 120 receives an encoded bitstream from source device 110. The encoded bitstream may include signaling information defined by GPCC encoder 116, which is also used by GPCC decoder 126, such as syntax elements having values that represent a point cloud. Data consumer 122 uses the decoded data. For example, data consumer 122 may use the decoded point cloud data to determine the locations of physical objects. In some examples, data consumer 122 may comprise a display to present imagery based on the point cloud data.

GPCC encoder 116 and GPCC decoder 126 each may be implemented as any of a variety of suitable encoder and/or decoder circuitry, such as one or more microprocessors, digital signal processors (DSPs) , application specific integrated circuits (ASICs) , field programmable gate arrays (FPGAs) , discrete logic, software, hardware, firmware or any combinations thereof. When the techniques are implemented partially in software, a device may store instructions for the software in a suitable, non-transitory computer-readable medium and execute the instructions in hardware using one or more processors to perform the techniques of this disclosure. Each of GPCC encoder 116 and GPCC decoder 126 may be included in one or more encoders or decoders, either of which may be integrated as part of a combined encoder/decoder (CODEC) in a respective device. A device including GPCC encoder 116 and/or GPCC decoder 126 may comprise one or more integrated circuits, microprocessors, and/or other types of devices.

GPCC encoder 116 and GPCC decoder 126 may operate according to a coding standard, such as video point cloud compression (VPCC) standard or a geometry point cloud compression (GPCC) standard. This disclosure may generally refer to coding (e.g., encoding and decoding) of frames to include the process of encoding or decoding data. An encoded bitstream generally includes a series of values for syntax elements representative of coding decisions (e.g., coding modes) .

A point cloud may contain a set of points in a 3D space, and may have attributes associated with the point. The attributes may be color information such as R, G, B or Y, Cb, Cr, or reflectance information, or other attributes. Point clouds may be captured by a variety of cameras or sensors such as LIDAR sensors and 3D scanners and may also be computer-generated. Point cloud data are used in a variety of applications including, but not limited to, construction (modeling) , graphics (3D models for visualizing and animation) , and the automotive industry (LIDAR sensors used to help in navigation) .

Fig. 2 is a block diagram illustrating an example of a GPCC encoder 200, which may be an example of the GPCC encoder 116 in the system 100 illustrated in Fig. 1, in accordance with some embodiments of the present disclosure. Fig. 3 is a block diagram illustrating an example of a GPCC decoder 300, which may be an example of the GPCC decoder 126 in the system 100 illustrated in Fig. 1, in accordance with some embodiments of the present disclosure.

In both GPCC encoder 200 and GPCC decoder 300, point cloud positions are coded first. Attribute coding depends on the decoded geometry. In Fig. 2 and Fig. 3, the region adaptive hierarchical transform (RAHT) unit 218, surface approximation analysis unit 212, RAHT unit 314 and surface approximation synthesis unit 310 are options typically used for Category 1 data. The level-of-detail (LOD) generation unit 220, lifting unit 222, LOD generation unit 316 and inverse lifting unit 318 are options typically used for Category 3 data. All the other units are common between Categories 1 and 3.

For Category 3 data, the compressed geometry is typically represented as an octree from the root all the way down to a leaf level of individual voxels. For Category 1 data, the compressed geometry is typically represented by a pruned octree (i.e., an octree from the root down to a leaf level of blocks larger than voxels) plus a model that approximates the surface within each leaf of the pruned octree. In this way, both Category 1 and 3 data share the octree coding mechanism, while Category 1 data may in addition approximate the voxels within each leaf with a surface model. The surface model used is a triangulation comprising 1-10 triangles per block, resulting in a triangle soup. The Category 1 geometry codec is therefore known as the Trisoup geometry codec, while the Category 3 geometry codec is known as the Octree geometry codec.

In the example of Fig. 2, GPCC encoder 200 may include a coordinate transform unit 202, a color transform unit 204, a voxelization unit 206, an attribute transfer unit 208, an octree analysis unit 210, a surface approximation analysis unit 212, an arithmetic encoding unit 214, a geometry reconstruction unit 216, an RAHT unit 218, a LOD generation unit 220, a lifting unit 222, a coefficient quantization unit 224, and an arithmetic encoding unit 226.

As shown in the example of Fig. 2, GPCC encoder 200 may receive a set of positions and a set of attributes. The positions may include coordinates of points in a point cloud. The attributes may include information about points in the point cloud, such as colors associated with points in the point cloud.

Coordinate transform unit 202 may apply a transform to the coordinates of the points to transform the coordinates from an initial domain to a transform domain. This disclosure may refer to the transformed coordinates as transform coordinates. Color transform unit 204 may apply a transform to convert color information of the attributes to a different domain. For example, color transform unit 204 may convert color information from an RGB color space to a YCbCr color space.

Furthermore, in the example of Fig. 2, voxelization unit 206 may voxelize the transform coordinates. Voxelization of the transform coordinates may include quantizing and removing some points of the point cloud. In other words, multiple points of the point cloud may be subsumed within a single “voxel, ” which may thereafter be treated in some respects as one point. Furthermore, octree analysis unit 210 may generate an octree based on the voxelized transform coordinates. Additionally, in the example of Fig. 2, surface approximation analysis unit 212 may analyze the points to potentially determine a surface representation of sets of the points. Arithmetic encoding unit 214 may perform arithmetic encoding on syntax elements representing the information of the octree and/or surfaces determined by surface approximation analysis unit 212. GPCC encoder 200 may output these syntax elements in a geometry bitstream.

Geometry reconstruction unit 216 may reconstruct transform coordinates of points in the point cloud based on the octree, data indicating the surfaces determined by surface approximation analysis unit 212, and/or other information. The number of transform coordinates reconstructed by geometry reconstruction unit 216 may be different from the original number of points of the point cloud because of voxelization and surface approximation. This disclosure may refer to the resulting points as reconstructed points. Attribute transfer unit 208 may transfer attributes of the original points of the point cloud to reconstructed points of the point cloud data.

Furthermore, RAHT unit 218 may apply RAHT coding to the attributes of the reconstructed points. Alternatively or additionally, LOD generation unit 220 and lifting unit 222 may apply LOD processing and lifting, respectively, to the attributes of the reconstructed points. RAHT unit 218 and lifting unit 222 may generate coefficients based on the attributes. Coefficient quantization unit 224 may quantize the coefficients generated by RAHT unit 218 or lifting unit 222. Arithmetic encoding unit 226 may apply arithmetic coding to syntax elements representing the quantized coefficients. GPCC encoder 200 may output these syntax elements in an attribute bitstream.

In the example of Fig. 3, GPCC decoder 300 may include a geometry arithmetic decoding unit 302, an attribute arithmetic decoding unit 304, an octree synthesis unit 306, an inverse quantization unit 308, a surface approximation synthesis unit 310, a geometry reconstruction unit 312, a RAHT unit 314, a LOD generation unit 316, an inverse lifting unit 318, a coordinate inverse transform unit 320, and a color inverse transform unit 322.

GPCC decoder 300 may obtain a geometry bitstream and an attribute bitstream. Geometry arithmetic decoding unit 302 of decoder 300 may apply arithmetic decoding (e.g., CABAC or other type of arithmetic decoding) to syntax elements in the geometry bitstream. Similarly, attribute arithmetic decoding unit 304 may apply arithmetic decoding to syntax elements in attribute bitstream.

Octree synthesis unit 306 may synthesize an octree based on syntax elements parsed from geometry bitstream. In instances where surface approximation is used in geometry bitstream, surface approximation synthesis unit 310 may determine a surface model based on syntax elements parsed from geometry bitstream and based on the octree.

Furthermore, geometry reconstruction unit 312 may perform a reconstruction to determine coordinates of points in a point cloud. Coordinate inverse transform unit 320 may apply an inverse transform to the reconstructed coordinates to convert the reconstructed coordinates (positions) of the points in the point cloud from a transform domain back into an initial domain.

Additionally, in the example of Fig. 3, inverse quantization unit 308 may inverse quantize attribute values. The attribute values may be based on syntax elements obtained from attribute bitstream (e.g., including syntax elements decoded by attribute arithmetic decoding unit 304) .

Depending on how the attribute values are encoded, RAHT unit 314 may perform RAHT coding to determine, based on the inverse quantized attribute values, color values for points of the point cloud. Alternatively, LOD generation unit 316 and inverse lifting unit 318 may determine color values for points of the point cloud using a level of detail-based technique.

Furthermore, in the example of Fig. 3, color inverse transform unit 322 may apply an inverse color transform to the color values. The inverse color transform may be an inverse of a color transform applied by color transform unit 204 of encoder 200. For example, color transform unit 204 may transform color information from an RGB color space to a YCbCr color space. Accordingly, color inverse transform unit 322 may transform color information from the YCbCr color space to the RGB color space.

The various units of Fig. 2 and Fig. 3 are illustrated to assist with understanding the operations performed by encoder 200 and decoder 300. The units may be implemented as fixed-function circuits, programmable circuits, or a combination thereof. Fixed-function circuits refer to circuits that provide particular functionality and are preset on the operations that can be performed. Programmable circuits refer to circuits that can be programmed to perform various tasks and provide flexible functionality in the operations that can be performed. For instance, programmable circuits may execute software or firmware that cause the programmable circuits to operate in the manner defined by instructions of the software or firmware. Fixed-function circuits may execute software instructions (e.g., to receive parameters or output parameters) , but the types of operations that the fixed-function circuits perform are generally immutable. In some examples, one or more of the units may be distinct circuit blocks (fixed-function or programmable) , and in some examples, one or more of the units may be integrated circuits.

Some example embodiments of the present disclosure will be described in detailed hereinafter. It should be understood that section headings are used in the present document to facilitate ease of understanding and do not limit the embodiments disclosed in a section to only that section. Furthermore, while certain embodiments are described with reference to GPCC or other specific point cloud codecs, the disclosed techniques are applicable to other point cloud coding technologies also. Furthermore, while some embodiments describe point cloud coding steps in detail, it will be understood that corresponding steps decoding that undo the coding will be implemented by a decoder.
1. Brief Summary

The present disclosure is related to point cloud coding technologies. Specifically, it is related to point cloud attribute compression. The ideas may be applied individually or in various combination, to any point cloud coding standard or non-standard point cloud codec, e.g., the being-developed Geometry based Point Cloud Compression (G-PCC) .
2. Abbreviations
G-PCC  Geometry based Point Cloud Compression
MPEG Moving Picture Experts Group
3DG  3D Graphics Coding Group
CFP   Call For Proposal
V-PCC  Video-based Point Cloud Compression
RAHT  Region-Adaptive Hierarchical Transform
3. Introduction

MPEG, short for Moving Picture Experts Group, is one of the main standardization groups dealing with multimedia. In 2017, the MPEG 3D Graphics Coding group (3DG) published a call for proposals (CFP) document to start to develop point cloud coding standard (MPEG 3DG and Requirements, “Call for Proposals for Point Cloud Compression V2” , ISO/IEC JTC1/SC29 WG11 N16763) . The final standard will consist in two classes of solutions. Video-based Point Cloud Compression (V-PCC) is appropriate for point sets with a relatively uniform distribution of points (ISO/IEC JTC 1/SC 29/WG 07, “Information technology -Coded Representation of Immersive Media -Part 5: Visual Volumetric Video-based Coding (V3C) and Video-based Point Cloud Compression (V-PCC) ” , ISO/IEC 23090-5) . Geometry-based Point Cloud Compression (G-PCC) is appropriate for more sparse distributions (ISO/IEC JTC 1/SC 29/WG 11, “Information technology -MPEG-I (Coded Representation of Immersive Media) -Part 9: Geometry-based Point Cloud Compression” , ISO/IEC 23090-9: 2020 (E) ) . Both V-PCC and G-PCC support the coding and decoding for single point cloud and point cloud sequence.

In one point cloud, there may be geometry information and attribute information. Geometry information is used to describe the geometry locations of the data points. Attribute information is used to record some details of the data points, such as textures, normal vectors, reflections and so on. In GPCC, the smallest independent unit for point cloud encoding and decoding is slice. On the decoding side, for each slice, the geometry information of the slice will be reconstructed first, and then the attribute information of the slice will be reconstructed.
3.1 Slice attributes

In G-PCC, the reconstruction of a single slice attribute for the coded slice geometry starts with geometry coordinates conversion. Attribute coding can use either the slice's reconstructed STV point positions or the points's caled angular coordinates.

The expression AttrPos [ptIdx] [k] specifies the coordinates of each point for attribute coding. When attr_coord_conv_enabled is 0 and attr_inter_prediction_enabled is 0, AttrPos is equivalent to PointPos, which is the slice geometry in the slice’s coordinate system. When attr_coord_conv_enabled is 0 and attr_inter_prediction_enabled is 1, AttrPos is equivalent to the slice geometry translated to the coding coordinate system by the addition of the slice origin, SliceOrigin. Otherwise, AttrPos [ptIdx] [k] are angular point coordinates.

The conversion is specified by the expression AttrPosAng [ptIdx] [k] . When geom_tree_type is equal to 1 and slice_attr_inter_prediction is equal to 1, the point’s angular coordinates shall be offset by the minimum value between the minimum angular coordinates of the current slice and previously applied offset value. The minimum angular coordinates of the current slice are specified by the expression MinCurAng [k] . The previously applied offset value is specified by the expression MinRefAng [k] . The minimum value between MinRefAng and MinCurAng is specified by the expression MinAng [k] .

Otherwise, the point's angular coordinates shall be offset by the minimum angular coordinates.

The offset coordinates shall be scaled. Any negative coordinate after conversion shall be clipped to 0.
AttrPosAng [ptIdx] [k] : = DivExp2Up (relPos × attr_coord_conv_scale [k] , 8)
where
relPos : = Max (0, PointAng [ptIdx] [k] –minAng [k] )
MinAng [k] : = geom_tree_type == 1 &&slice_attr_inter_prediction == 1
?Min (MinCurAng [k] , MinRefAng [k] ) : MinCurAng [k]
MinCurAng [k] : = geom_tree_type == 1 &&k == 1
?-Exp2 (ptree_ang_azimuth_pi_bits_minus11 + 10)
:0

It is a requirement of bitstream conformance that attr_coord_conv_scale shall not cause any converted coordinate, AttrPosAng [ptIdx] [k] , to be greater than Exp2 (MaxSliceDimLog2) -1.

When geom_tree_type is equal to 1, after the coordinate’s conversion of a slice, MinRefAng shall be set equal to MinAng.
3.2 Reference slice generation for attribute

In G-PCC, the attribute values and coordinates of the reference slice are derived based on the bounding box of the coordinates of the current slice. Only points in the reference frame that are within the geometry edge box range of the current slice will be included in the reference slice.
3.3 Coordinate preprocessing for RAHT

In G-PCC, one of important point cloud attribute coding tools is the Region-Adaptive Hierarchical Transform (RAHT) . It is a transform that uses the attributes associated with a node in a lower level of the octree to predict the attributes of the nodes in the next level. It assumes that the positions of the points are given at both the encoder and decoder. RAHT follows the octree scan backwards, from leaf nodes to root node, at each step recombining nodes into larger ones until reaching the root node. At each level of octree, the nodes are processed in the Morton order. At each decomposition, instead of grouping eight nodes at a time, RAHT does it in three steps along each dimension, (e.g., along z, then y then x) . If there are L levels in octree, RAHT takes 3L levels to traverse the tree backwards. In order to perform the above processing, it is necessary to perform coordinate preprocesses, including converting the geometry coordinates of the points into Morton codes and sorting the points according to the Morton codes.
4. Problems

The existing designs for point cloud attribute compression have the following problems:
1. In current design, the coordinates conversion is performed every time when the reconstruction of a
single slice attribute starts. However, when there are multiple kinds of attribute for one slice, the coor-dinate conversion process may be repeated unnecessarily.
2. In current design, the reference slice generation is performed every time when the reconstruction of a
single slice attribute starts and inter prediction is enabled for the point cloud sequence. However, when there are multiple kinds of attribute for one slice, the reference slice generation may be repeated unnec-essarily.
3. In current design, the coordinate preprocessing for RAHT is performed every time when the reconstruc-
tion of a single slice attribute starts and RAHT is used for attribute reconstruction. However, when there are multiple kinds of attribute for one slice, the coordinate preprocessing for RAHT may be repeated unnecessarily.
5. Detailed solutions

To solve the above problems and some other problems not mentioned, methods as summarized below are disclosed. The embodiments should be considered as examples to explain the general concepts and should not be interpreted in a narrow way. Furthermore, these embodiments can be applied individually or combined in any manner. The processing unit described below may include but be not limited to slice/tile/frame and so on.
1) It is proposed to perform some specific processes only for some attributes of one processing unit when the
process unit is with multiple attributes.
a. In one example, there may be multiple attributes for one processing unit.
b. In one example, there may be some specific information needed for the coding of one attribute.
i. In one example, the specific information may be the coordinates used for attribute coding.
ii. In one example, the specific information may be the level of details structure build for
attribute coding.
iii. In one example, the specific information may be the searched predictors and the predictors’
characters (such as, predictor weight) used for attribute coding.
iv. In one example, the specific information may be the sorted points based on the coordinates
used for attribute coding.
v. In one example, the specific information may be the RAHT transform tree structure used
for RAHT attribute coding.
vi. In one example, the specific information may be the reference processing unit used for
attribute inter prediction.
c. In one example, there may be some processes to generate the specific information.
d. In one example, there may be some input for the process (es) to generate the specific information.
i. In one example, the input may be the parameters to control/be used in the process (es) .
ii. In one example, the input may be the input information needed to generate the specific
information.
1. In one example, the input information may be the original coordinates for attribute
coding.
2. In one example, the input information may be the original reference frame (s) .
3. In one example, the input information may be the unsorted points based on the
coordinates.
4. In one example, the input information may be the points used for RAHT transform
tree building.
e. It is proposed to perform the process (es) only for the specific attribute (s) for one processing unit
when the process (es) is (are) determined to be enabled to be skipped for some attributes.
f. In one example, there may be some methods to determine whether the process (es) shall be per-
formed for one attribute.
i. In one example, if there is no previously coded attribute, the process (es) may be performed
for the current attribute.
ii. In one example, if the input of the process (es) of the current attribute is not as same as any
one of the inputs of the process (es) of the previously coded attributes, the process (es) may be performed for the current attribute.
g. In one example, there may be some methods to determine whether the process (es) shall be skipped
for one attribute.
i. In one example, if the input of the process (es) of the current attribute is as same as one of
the inputs of the process (es) of the previously coded attributes, the process (es) may be skipped for the current attribute.
ii. In one example, if the input of the process (es) of the current attribute is partly as same as
one of the inputs of the process (es) of the previously coded attributes, the process (es) may be skipped for the current attribute.
h. In one example, the process (es) may be performed for one specific attribute and the process (es)
may be skipped for the other attribute (s) .
i. In one example, the one specific attribute may be the first attribute of the processing unit
in coding order.
ii. In one example, the other attribute (s) may inherit the specific information from the first
attribute in coding order.
i. In one example, the process (es) may be performed for some specific attribute (s) and the process (es)
may be skipped for the other attributes.
i. In one example, which attributes that apply the process (es) may be derived at the encoder.
ii. In one example, which attributes that apply the process (es) may be derived at the decoder.
iii. In one example, which attributes that apply the process (es) may be signalled to the decoder.
iv. Alternatively, which attributes that skip the process (es) may be signalled to the decoder.
v. In one example, the attributes that skip the process (es) may inherit the specific information
from one of the attributes that apply the process (es) .
1. In one example, for one attribute that skip the process (es) , which attribute it inherit
the specific information may be derived at the encoder.
2. In one example, for one attribute that skip the process (es) , which attribute it inherit
the specific information may be derived at the decoder.
3. In one example, for one attribute that skip the process (es) , which attribute it inherit
the specific information may be signalled to the encoder.
2) It is proposed to perform the level of detail (s) generation and predictors search process for attribute coding
once for one processing unit.
a. In one example, the level of detail (s) generation and predictors search process may be performed
when the inter prediction is used for attribute encoding and decoding.
b. In one example, there may be one indicator to indicate whether the current attribute is the first
attribute of one processing unit.
i. In one example, the indicator may be signalled or derived at the encoder.
ii. In one example, the indicator may be parsed or derived at the decoder.
c. In one example, the level of detail (s) generation and predictors search process may be performed
only when the current attribute is the first attribute information of the current processing unit that needs to be processed.
d. In one example, the level of detail (s) generation and predictors search process may be skipped when
the current attribute is not the first attribute information of the current processing unit that needs to be processed.
i. In one example, the level of detail (s) and searched predictor (s) can be inherited from the
level of detail (s) and searched predictor (s) used by the previous attribute information pro-cessed.
ii. In one example, the characters (such as predictor weight, predictor index, predictor order
and so on) of the searched predictor (s) can be inherited from those of the searched predic-tor(s) used by the previous attribute information processed.
3) It is proposed to perform the geometry coordinate preprocessing for attribute coding once for one processing
unit.
a. In one example, the geometry coordinate preprocessing may be performed when the angular coor-
dinate is used for attribute coding and decoding.
b. In one example, the geometry coordinate preprocessing may include procedures for generating co-
ordinates required for attribute encoding and decoding.
i. In one example, the geometry coordinate preprocessing may include the coordinates con-
versions.
1. In one example, the coordinates conversion may be the coordinates conversion of
the current processing unit.
2. In one example, the coordinates conversion may be the coordinates conversion of
the reference processing unit.
ii. In one example, the geometry coordinate preprocessing may include the coordinates scal-
ing and/or shifting.
1. In one example, the coordinates scaling and/or shifting may be the coordinates
scaling and/or shifting of the current processing unit.
2. In one example, the coordinates scaling and/or shifting may be the coordinates
scaling and/or shifting of the reference processing unit.
c. In one example, there may be one indicator to indicate whether the current attribute is the first
attribute of one processing unit.
i. In one example, the indicator may be signalled or derived at the encoder.
ii. In one example, the indicator may be parsed or derived at the decoder.
d. In one example, the geometry coordinate preprocessing may be performed only when the current
attribute is the first attribute information of the current processing unit that needs to be processed.
e. In one example, the geometry coordinate preprocessing may be skipped when the current attribute
is not the first attribute information of the current processing unit that needs to be processed.
i. In one example, the coordinates required for the current attribute encoding and decoding
can be inherited from the coordinates used by the previous attribute information processed.
4) It is proposed to perform the reference processing unit generation for attribute coding once for one pro-
cessing unit.
a. In one example, the reference processing unit generation may be performed when the inter predic-
tion is used for attribute encoding and decoding.
b. In one example, there may be one indicator to indicate whether the current attribute is the first
attribute of one processing unit.
i. In one example, the indicator may be signalled or derived at the encoder.
ii. In one example, the indicator may be parsed or derived at the decoder.
c. In one example, the reference processing unit generation may be performed only when the current
attribute is the first attribute information of the current processing unit that needs to be processed.
d. In one example, the reference processing unit generation may be skipped when the current attribute
is not the first attribute information of the current processing unit that needs to be processed.
i. In one example, the reference processing unit for the current attribute encoding and decod-
ing can be inherited from the reference processing unit used by the previous attribute in-formation processed.
5) It is proposed to perform the coordinate preprocessing for RAHT once for one processing unit.
a. In one example, the coordinate preprocessing for RAHT may be performed when the RAHT based
method is used for attribute coding and decoding.
b. In one example, the coordinate preprocessing for RAHT may include the preprocessing of the points
of the current processing unit and the reference processing unit required by RAHT.
i. In one example, one point may include coordinate and corresponding attribute (s) .
ii. In one example, the coordinate preprocessing for RAHT may include the Morton code
generation of coordinates.
1. In one example, the Morton code generation of coordinates may be the Morton
code generation of coordinates of the current processing unit.
2. In one example, the Morton code generation of coordinates may be the Morton
code generation of coordinates of the reference processing unit.
iii. In one example, the coordinate preprocessing for RAHT may include the reordering of the
points.
1. In one example, the reordering of the points may be the reordering of the points
of the current processing unit.
2. In one example, the reordering of the points may be the reordering of the points
of the reference processing unit.
3. In one example, the reordering may be based on the Morton codes of the points.
iv. In one example, the coordinate preprocessing for RAHT may include the relabeling of
attributes.
1. In one example, the relabeling of attributes may be the relabeling of attributes of
the current processing unit.
2. In one example, the relabeling of attributes may be the relabeling of attributes of
the reference processing unit.
3. In one example, the relabeling of attributes may be based on the coordinates or the
region structure.
c. In one example, there may be one indicator to indicate whether the current attribute is the first
attribute of one processing unit.
i. In one example, the indicator may be signalled or derived at the encoder.
ii. In one example, the indicator may be parsed or derived at the decoder.
d. In one example, the coordinate preprocessing for RAHT may be performed only when the current
attribute is the first attribute information of the current processing unit that needs to be processed.
e. In one example, the coordinate preprocessing for RAHT may be skipped when the current attribute
is not the first attribute information of the current processing unit that needs to be processed.
i. In one example, the points of the current processing unit and the reference processing unit
required by RAHT can be inherited from the points used by the previous attribute infor-mation processed.
6) It is proposed to append the reconstructed processing unit in processing unit level A to generate the recon-
structed processing unit in processing unit level B after all attributes of one processing unit in processing unit level A are reconstructed.
a. In one example, one processing unit in processing unit level B may include one or multiple pro-
cessing units in processing unit level A.
i. In one example, processing unit in processing unit level B may be one tile or frame and
processing unit in processing unit level A may be one slice.
7) Whether to and/or how to apply a method disclosed above may be signaled from encoder to decoder in a
bitstream/frame/tile/slice/octree/etc.
8) Whether to and/or how to apply the disclosed methods above may be dependent on coded information, such
as dimensions, colour format, colour component, slice/picture type.

Fig. 4 illustrates a flowchart of a method 400 for point cloud coding in accordance with embodiments of the present disclosure. The method 400 is implemented during a conversion between a point cloud (PC) sample of a point cloud sequence and a bitstream of the point cloud sequence.

At block 410, for a conversion between a PC sample of a point cloud sequence and a bitstream of the point cloud sequence, it is determined that a target process is performed for a set of attributes in a plurality of attributes of a processing unit associated with the PC sample.

At block 420, the conversion is performed based on the set of processed attributes. In some embodiments, the conversion may include encoding the PC sample into the bitstream. Alternatively, the conversion may include decoding the PC sample from the bitstream.

The method 400 enables a target process to be performed for the set of attributes in the plurality of attributes of the processing unit. Compared with the conventional solution, the method 400 can advantageously improve the coding efficiency of point cloud coding.

In some embodiments, there may be multiple attributes for one processing unit. In some other embodiments, target information may be used for coding of an attribute of the processing unit. In some embodiments, the target information may include one or more coordinates used for the coding of the attribute of the processing unit. In some other embodiments, the target information may include a level of details structure constructed for the coding of the attribute of the processing unit. Alternatively, the target information may include at least one of a searched predictor or a character of the predictor used for the coding of the attribute of the processing unit. For example, the character of the predictor may include a predictor weight. In some embodiments, the target information may include one or more sorted points based on one or more coordinates used for the coding of the attribute of the processing unit. In some other embodiments, the target information may include a region-adaptive hierarchical transform (RAHT) transform tree structure used for RAHT attribute coding. Alternatively, the target information may include a reference processing unit used for an inter prediction of the attribute of the processing unit.

In some embodiments, the target process may be used to generate target information for coding of an attribute of the processing unit. In some other embodiments, an input for the target process may be used to generate target information for coding of an attribute of the processing unit. As an example, the input may include a parameter to control the target process. Alternatively, the input may include a parameter to be used in the target process. As another example, the input may include input information used to generate the target information. For example, the input information may include at least one of the following: an original coordinate for attribute coding, one or more original reference frames, one or more unsorted points based on one or more coordinates, or one or more points used for RAHT transform tree building.

In some embodiments, if the target process is determined to be enabled to be skipped for one or more attributes, the target process may be performed for a target attribute of the processing unit. In some other embodiments, an approach may be used to determine whether the target process is performed for an attribute of the processing unit. As an example, if there is no previously coded attribute for the processing unit, the target process may be performed for a current attribute of the processing unit. Alternatively, if an input of the target process for a current attribute of the processing unit is different from an input of the target process for a previously coded attribute of the processing unit, the target process may be performed for the current attribute of the processing unit.

In some other embodiments, an approach may be used to determine whether the target process is skipped for an attribute of the processing unit. As an example, if an input of the target process for a current attribute of the processing unit is same as an input of the target process for a previously coded attribute of the processing unit, the target process may be skipped for the current attribute of the processing unit. Alternatively, if an input of the target process for a current attribute of the processing unit is partly same as an input of the target process for a previously coded attribute of the processing unit, the target process may be skipped for the current attribute of the processing unit.

In some embodiments, the target process may be performed for a target attribute of the processing unit, and the target process may be skipped for another attribute of the processing unit. For example, the target attribute may be the first attribute of the processing unit in coding order. Alternatively, the other attribute may inherit target information for attribute coding of the processing unit from the first attribute in coding order.

In some embodiments, the target attribute for which the target process is performed may be derived at an encoder. In some embodiments, the target attribute for which the target process is performed may be derived at a decoder. In some other embodiments, the target attribute for which the target process is performed may be signalled to a decoder. Alternatively, the other attribute for which the target process is skipped is signalled to a decoder.

In some embodiments, the other attribute for which the target process is skipped may inherit target information for attribute coding of the processing unit from the target attribute for which the target process is performed. As an example, the target attribute which the other attribute inherits the target information from may be derived at an encoder. As another example, the target attribute which the other attribute inherits the target information from may be derived at a decoder. Alternatively, the target attribute which the other attribute inherits the target information from may be signalled to an encoder.

In some embodiments, information of whether to and/or how to determine that the target process is performed for the set of attributes in the plurality of attributes of the processing unit may be signaled from an encoder to a decoder in one of the followings: a bitstream, a frame, a tile, a slice, or an octree. In some embodiments, the method 400 may further include: determining, based on coded information of the PC sample of the point cloud sequence, whether to and/or how to determine that the target process is performed for the set of attributes in the plurality of attributes of the processing unit. The coded information may include at least one of: a dimension, a colour format, a colour component, a slice type, or a picture type.

According to further embodiments of the present disclosure, a non-transitory computer-readable recording medium is provided. The non-transitory computer-readable recording medium stores a bitstream of a point cloud sequence which is generated by a method performed by a point cloud processing apparatus. The method comprises: determining that a target process is performed for a set of attributes in a plurality of attributes of a processing unit associated with a point cloud (PC) sample of the point cloud sequence; and generating the bitstream based on the set of processed attributes.

According to still further embodiments of the present disclosure, a method for storing bitstream of a point cloud sequence is provided. The method comprises: determining that a target process is performed for a set of attributes in a plurality of attributes of a processing unit associated with a point cloud (PC) sample of the point cloud sequence; generating the bitstream based on the set of processed attributes; and storing the bitstream in a non-transitory computer-readable recording medium.

Fig. 5 illustrates a flowchart of a method 500 for point cloud coding in accordance with embodiments of the present disclosure. The method 500 is implemented during a conversion between a point cloud (PC) sample of a point cloud sequence and a bitstream of the point cloud sequence.

At block 510, for a conversion between a PC sample of a point cloud sequence and a bitstream of the point cloud sequence, a process is performed for an attribute coding for a processing unit associated with the PC sample. In this case, the process may include at least one of: a generation of a level of a detail, a process of predictor search, a preprocessing of a geometry coordinate, a generation of a reference processing unit, or a coordinate preprocessing for a region-adaptive hierarchical transform (RAHT) .

At block 520, the conversion is performed based on the processed processing unit. In some embodiments, the conversion may include encoding the PC sample into the bitstream. Alternatively, the conversion may include decoding the PC sample from the bitstream.

The method 500 enables the process to be performed for an attribute coding of a processing unit. Compared with the conventional solution, the method 500 can advantageously improve the coding efficiency of point cloud coding.

In some embodiments, if an inter prediction is used for the attribute coding, the generation of the level of the detail and/or the process of the predictor search may be performed. In some embodiments, if an angular coordinate is used for the attribute coding, the preprocessing of the geometry coordinate may be performed. In some other embodiments, if an inter prediction is used for the attribute coding, the generation of the reference processing unit may be performed. Alternatively, if a RAHT based approach is used for the attribute coding, the coordinate preprocessing for the RAHT may be performed.

In some embodiments, the preprocessing of the geometry coordinate may include a procedure for generating a coordinate required for the attribute coding. In some embodiments, the preprocessing of the geometry coordinate may include a coordinate conversion. For example, the coordinate conversion may include a coordinate conversion of a current processing unit. Alternatively, the coordinate conversion may include a coordinate conversion of a reference processing unit. In some other embodiments, the preprocessing of the geometry coordinate may include at least one of: a coordinate scaling or a coordinate shifting. For example, the coordinate scaling and/or the coordinate shifting may include at least one of: a coordinate scaling of a current processing unit, or a coordinate shifting of the current processing unit. Alternatively, the coordinate scaling and/or the coordinate shifting may include at least one of: a coordinate scaling of a reference processing unit, or a coordinate shifting of the reference processing unit.

In some embodiments, the coordinate preprocessing for the RAHT may include at least one of: a preprocessing of a point of a current processing unit required by the RAHT, or a preprocessing of a point of a reference processing unit required by the RAHT. In some embodiments, the point of the current processing unit and/or the point of the reference processing unit may include at least one of a coordinate or a corresponding attribute. In some other embodiments, the coordinate preprocessing for the RAHT may include a Morton code generation of a coordinate. For example, the Morton code generation of coordinate may include a Morton code generation of coordinate for the current processing unit. Alternatively, the Morton code generation of coordinate may include a Morton code generation of coordinate for the reference processing unit.

In some embodiments, the coordinate preprocessing for the RAHT may include a reordering of one or more points. As an example, the reordering of the one or more point may include a reordering of one or more points for the current processing unit. Alternatively, the reordering of the one or more point may include a reordering of one or more points for the reference processing unit. In some examples, the reordering may be based on a Morton code of the point.

In some embodiments, the coordinate preprocessing for the RAHT may include a relabeling of an attribute. As an example, the relabeling of the attribute may include a relabeling of an attribute for the current processing unit. Alternatively, the relabeling of the attribute may include a relabeling of an attribute for the reference processing unit. In some examples, the relabeling of the attribute may be based on at least one of a coordinate or a region structure.

In some embodiments, an indicator may be used to indicate whether a current attribute is the first attribute of the processing unit. For example, the indicator may be signalled and/or derived at an encoder. Alternatively, the indicator may be parsed or derived at a decoder.

In some embodiments, if a current attribute is the first attribute information of a current processing unit that needs to be processed, the process may be performed. Alternatively, if a current attribute is not the first attribute information of a current processing unit that needs to be processed, the process may be skipped. For example, for the generation of the level of the detail and/or the process of the predictor search, the level of the detail and/or the searched predictor may be inherited from a level of a detail and/or a searched predictor used by a processed previous attribute information. As another example, for the generation of the level of the detail and/or the process of the predictor search, a character (such as a predictor weight, a predictor index, a predictor order and the like) of the searched predictor may be inherited from a searched predictor used by a processed previous attribute information.

In some embodiments, for the preprocessing of the geometry coordinate, a coordinate required for a current attribute coding may be inherited from a coordinate used by a processed previous attribute information. In some other embodiments, for the generation of the reference processing unit, a reference processing unit for a current attribute coding may be inherited from a reference processing unit used by a processed previous attribute information. In some embodiments, for the coordinate preprocessing for the RAHT, a point of a current processing unit and/or a point of a reference processing unit may be inherited from a point used by a processed previous attribute information.

In some embodiments, information of whether to and/or how to perform the process for the attribute coding for the processing unit associated with the PC sample may be signaled from an encoder to a decoder in one of the followings: a bitstream, a frame, a tile, a slice, or an octree. In some embodiments, the method 500 may further include: determining, based on coded information of the PC sample of the point cloud sequence, whether to and/or how to perform the process for the attribute coding for the processing unit associated with the PC sample. The coded information may include at least one of: a dimension, a colour format, a colour component, a slice type, or a picture type.

According to further embodiments of the present disclosure, a non-transitory computer-readable recording medium is provided. The non-transitory computer-readable recording medium stores a bitstream of a point cloud sequence which is generated by a method performed by a point cloud processing apparatus. The method comprises: performing a process for an attribute coding for a processing unit associated with a point cloud (PC) sample of the point cloud sequence, wherein the process comprises at least one of: a generation of a level of a detail, a process of predictor search, a preprocessing of a geometry coordinate, a generation of a reference processing unit, or a coordinate preprocessing for a region-adaptive hierarchical transform (RAHT) ; and generating the bitstream based on the processed processing unit.

According to still further embodiments of the present disclosure, a method for storing bitstream of a point cloud sequence is provided. The method comprises: performing a process for an attribute coding for a processing unit associated with a point cloud (PC) sample of the point cloud sequence, wherein the process comprises at least one of: a generation of a level of a detail, a process of predictor search, a preprocessing of a geometry coordinate, a generation of a reference processing unit, or a coordinate preprocessing for a region-adaptive hierarchical transform (RAHT) ; generating the bitstream based on the processed processing unit; and storing the bitstream in a non-transitory computer-readable recording medium.

Fig. 6 illustrates a flowchart of a method 600 for point cloud coding in accordance with embodiments of the present disclosure. The method 600 is implemented during a conversion between a point cloud (PC) sample of a point cloud sequence and a bitstream of the point cloud sequence.

At block 610, for a conversion between a PC sample of a point cloud sequence and a bitstream of the point cloud sequence, a reconstructed processing unit in a first processing unit level associated with the PC sample is generated by adding a reconstructed processing unit in a second processing unit level. In this case, attributes of a processing unit in the second processing unit level are reconstructed.

At block 620, the conversion is performed based on the reconstructed processing unit in the first processing unit level. In some embodiments, the conversion may include encoding the PC sample into the bitstream. Alternatively, the conversion may include decoding the PC sample from the bitstream.

The method 600 enables a reconstructed processing unit in a first processing unit level to be generated by adding a reconstructed processing unit in a second processing unit level. Compared with the conventional solution, the method 600 can advantageously improve the coding efficiency of point cloud coding.

In some embodiments, a processing unit in the first processing unit level may include at least one processing unit in the second processing unit level. For example, the processing unit in the first processing unit level may include at least one of a tile or a frame. Alternatively or additionally, the processing unit in the second processing unit level may include a slice.

In some embodiments, information of whether to and/or how to generate the reconstructed processing unit in the first processing unit level associated with the PC sample by adding the reconstructed processing unit in the second processing unit level may be signaled from an encoder to a decoder in one of the followings: a bitstream, a frame, a tile, a slice, or an octree. In some embodiments, the method 600 may further include: determining, based on coded information of the PC sample of the point cloud sequence, whether to and/or how to generate the reconstructed processing unit in the first processing unit level associated with the PC sample by adding the reconstructed processing unit in the second processing unit level. The coded information including at least one of: a dimension, a colour format, a colour component, a slice type, or a picture type.

According to further embodiments of the present disclosure, a non-transitory computer-readable recording medium is provided. The non-transitory computer-readable recording medium stores a bitstream of a point cloud sequence which is generated by a method performed by a point cloud processing apparatus. The method comprises: generating a reconstructed processing unit in a first processing unit level associated with a point cloud (PC) sample of the point cloud sequence by adding a reconstructed processing unit in a second processing unit level, wherein attributes of a processing unit in the second processing unit level are reconstructed; and generating the bitstream based on the reconstructed processing unit in the first processing unit level.

According to still further embodiments of the present disclosure, a method for storing bitstream of a point cloud sequence is provided. The method comprises: generating a reconstructed processing unit in a first processing unit level associated with a point cloud (PC) sample of the point cloud sequence by adding a reconstructed processing unit in a second processing unit level, wherein attributes of a processing unit in the second processing unit level are reconstructed; generating the bitstream based on the reconstructed processing unit in the first processing unit level; and storing the bitstream in a non-transitory computer-readable recording medium.

Implementations of the present disclosure can be described in view of the following clauses, the features of which can be combined in any reasonable manner.

Clause 1. A method for point cloud coding, comprising: determining, for a conversion between a point cloud (PC) sample of a point cloud sequence and a bitstream of the point cloud sequence, that a target process is performed for a set of attributes in a plurality of attributes of a processing unit associated with the PC sample; and performing the conversion based on the set of processed attributes.

Clause 2. The method of clause 1, wherein there is multiple attributes for one processing unit.

Clause 3. The method of clause 1, wherein target information is used for coding of an attribute of the processing unit.

Clause 4. The method of clause 3, wherein the target information comprises one or more coordinates used for the coding of the attribute of the processing unit.

Clause 5. The method of clause 3, wherein the target information comprises a level of details structure constructed for the coding of the attribute of the processing unit.

Clause 6. The method of clause 3, wherein the target information comprises at least one of a searched predictor or a character of the predictor used for the coding of the attribute of the processing unit.

Clause 7. The method of clause 6, wherein the character of the predictor comprises a predictor weight.

Clause 8. The method of clause 3, wherein the target information comprises one or more sorted points based on one or more coordinates used for the coding of the attribute of the processing unit.

Clause 9. The method of clause 3, wherein the target information comprises a region-adaptive hierarchical transform (RAHT) transform tree structure used for RAHT attribute coding.

Clause 10. The method of clause 3, wherein the target information comprises a reference processing unit used for an inter prediction of the attribute of the processing unit.

Clause 11. The method of clause 1, wherein the target process is used to generate target information for coding of an attribute of the processing unit.

Clause 12. The method of clause 1, wherein an input for the target process is used to generate target information for coding of an attribute of the processing unit.

Clause 13. The method of clause 12, wherein the input comprises a parameter to control the target process, or wherein the input comprises a parameter to be used in the target process.

Clause 14. The method of clause 12, wherein the input comprises input information used to generate the target information.

Clause 15. The method of clause 14, wherein the input information comprises at least one of the following: an original coordinate for attribute coding, one or more original reference frames, one or more unsorted points based on one or more coordinates, or one or more points used for RAHT transform tree building.

Clause 16. The method of clause 1, wherein if the target process is determined to be enabled to be skipped for one or more attributes, the target process is performed for a target attribute of the processing unit.

Clause 17. The method of clause 1, wherein an approach is used to determine whether the target process is performed for an attribute of the processing unit.

Clause 18. The method of clause 17, wherein if there is no previously coded attribute for the processing unit, the target process is performed for a current attribute of the processing unit.

Clause 19. The method of clause 17, wherein if an input of the target process for a current attribute of the processing unit is different from an input of the target process for a previously coded attribute of the processing unit, the target process is performed for the current attribute of the processing unit.

Clause 20. The method of clause 1, wherein an approach is used to determine whether the target process is skipped for an attribute of the processing unit.

Clause 21. The method of clause 20, wherein if an input of the target process for a current attribute of the processing unit is same as an input of the target process for a previously coded attribute of the processing unit, the target process is skipped for the current attribute of the processing unit.

Clause 22. The method of clause 20, wherein if an input of the target process for a current attribute of the processing unit is partly same as an input of the target process for a previously coded attribute of the processing unit, the target process is skipped for the current attribute of the processing unit.

Clause 23. The method of clause 1, wherein the target process is performed for a target attribute of the processing unit, and the target process is skipped for another attribute of the processing unit.

Clause 24. The method of clause 23, wherein the target attribute is the first attribute of the processing unit in coding order.

Clause 25. The method of clause 23, wherein the other attribute inherits target information for attribute coding of the processing unit from the first attribute in coding order.

Clause 26. The method of clause 23, wherein the target attribute for which the target process is performed is derived at an encoder.

Clause 27. The method of clause 23, wherein the target attribute for which the target process is performed is derived at a decoder.

Clause 28. The method of clause 23, wherein the target attribute for which the target process is performed is signalled to a decoder.

Clause 29. The method of clause 23, wherein the other attribute for which the target process is skipped is signalled to a decoder.

Clause 30. The method of clause 23, wherein the other attribute for which the target process is skipped inherits target information for attribute coding of the processing unit from the target attribute for which the target process is performed.

Clause 31. The method of clause 30, wherein the target attribute which the other attribute inherits the target information from is derived at an encoder.

Clause 32. The method of clause 30, wherein the target attribute which the other attribute inherits the target information from is derived at a decoder.

Clause 33. The method of clause 30, wherein the target attribute which the other attribute inherits the target information from is signalled to an encoder.

Clause 34. The method of any of clauses 1 to 33, wherein information of whether to and/or how to determine that the target process is performed for the set of attributes in the plurality of attributes of the processing unit is signaled from an encoder to a decoder in one of the followings: a bitstream, a frame, a tile, a slice, or an octree.

Clause 35. The method of any of clauses 1 to 33, further comprising: determining, based on coded information of the PC sample of the point cloud sequence, whether to and/or how to determine that the target process is performed for the set of attributes in the plurality of attributes of the processing unit, the coded information including at least one of: a dimension, a colour format, a colour component, a slice type, or a picture type.

Clause 36. A method for point cloud coding, comprising: performing, for a conversion between a point cloud (PC) sample of a point cloud sequence and a bitstream of the point cloud sequence, a process for an attribute coding for a processing unit associated with the PC sample, wherein the process comprises at least one of: a generation of a level of a detail, a process of predictor search, a preprocessing of a geometry coordinate, a generation of a reference processing unit, or a coordinate preprocessing for a region-adaptive hierarchical transform (RAHT) ; and performing the conversion based on the processed processing unit.

Clause 37. The method of clause 36, wherein if an inter prediction is used for the attribute coding, the generation of the level of the detail and/or the process of the predictor search is performed.

Clause 38. The method of clause 36, wherein if an angular coordinate is used for the attribute coding, the preprocessing of the geometry coordinate is performed.

Clause 39. The method of clause 36, wherein if an inter prediction is used for the attribute coding, the generation of the reference processing unit is performed.

Clause 40. The method of clause 36, wherein if a RAHT based approach is used for the attribute coding, the coordinate preprocessing for the RAHT is performed.

Clause 41. The method of clause 36, wherein the preprocessing of the geometry coordinate comprises a procedure for generating a coordinate required for the attribute coding.

Clause 42. The method of clause 41, wherein the preprocessing of the geometry coordinate comprises a coordinate conversion.

Clause 43. The method of clause 42, wherein the coordinate conversion comprises a coordinate conversion of a current processing unit.

Clause 44. The method of clause 42, wherein the coordinate conversion comprises a coordinate conversion of a reference processing unit.

Clause 45. The method of clause 41, wherein the preprocessing of the geometry coordinate comprises at least one of: a coordinate scaling or a coordinate shifting.

Clause 46. The method of clause 45, wherein the coordinate scaling and/or the coordinate shifting comprise at least one of: a coordinate scaling of a current processing unit, or a coordinate shifting of the current processing unit.

Clause 47. The method of clause 45, wherein the coordinate scaling and/or the coordinate shifting comprise at least one of: a coordinate scaling of a reference processing unit, or a coordinate shifting of the reference processing unit.

Clause 48. The method of clause 36, wherein the coordinate preprocessing for the RAHT comprises at least one of: a preprocessing of a point of a current processing unit required by the RAHT, or a preprocessing of a point of a reference processing unit required by the RAHT.

Clause 49. The method of clause 48, wherein the point of the current processing unit and/or the point of the reference processing unit comprise at least one of a coordinate or a corresponding attribute.

Clause 50. The method of clause 48, wherein the coordinate preprocessing for the RAHT comprises a Morton code generation of a coordinate.

Clause 51. The method of clause 50, wherein the Morton code generation of coordinate comprises a Morton code generation of coordinate for the current processing unit.

Clause 52. The method of clause 50, wherein the Morton code generation of coordinate comprises a Morton code generation of coordinate for the reference processing unit.

Clause 53. The method of clause 48, wherein the coordinate preprocessing for the RAHT comprises a reordering of one or more points.

Clause 54. The method of clause 53, wherein the reordering of the one or more point comprises a reordering of one or more points for the current processing unit.

Clause 55. The method of clause 53, wherein the reordering of the one or more point comprises a reordering of one or more points for the reference processing unit.

Clause 56. The method of clause 53, wherein the reordering is based on a Morton code of the point.

Clause 57. The method of clause 48, wherein the coordinate preprocessing for the RAHT comprises a relabeling of an attribute.

Clause 58. The method of clause 57, wherein the relabeling of the attribute comprises a relabeling of an attribute for the current processing unit.

Clause 59. The method of clause 57, wherein the relabeling of the attribute comprises a relabeling of an attribute for the reference processing unit.

Clause 60. The method of clause 57, wherein the relabeling of the attribute is based on at least one of a coordinate or a region structure.

Clause 61. The method of clause 36, wherein an indicator is used to indicate whether a current attribute is the first attribute of the processing unit.

Clause 62. The method of clause 61, wherein the indicator is signalled and/or derived at an encoder.

Clause 63. The method of clause 62, wherein the indicator is parsed or derived at a decoder.

Clause 64. The method of clause 36, wherein if a current attribute is the first attribute information of a current processing unit that needs to be processed, the process is performed.

Clause 65. The method of clause 36, wherein if a current attribute is not the first attribute information of a current processing unit that needs to be processed, the process is skipped.

Clause 66. The method of clause 65, wherein for the generation of the level of the detail and/or the process of the predictor search, the level of the detail and/or the searched predictor is inherited from a level of a detail and/or a searched predictor used by a processed previous attribute information.

Clause 67. The method of clause 65, wherein for the generation of the level of the detail and/or the process of the predictor search, a character of the searched predictor is inherited from a searched predictor used by a processed previous attribute information.

Clause 68. The method of clause 65, wherein for the preprocessing of the geometry coordinate, a coordinate required for a current attribute coding is inherited from a coordinate used by a processed previous attribute information.

Clause 69. The method of clause 65, wherein for the generation of the reference processing unit, a reference processing unit for a current attribute coding is inherited from a reference processing unit used by a processed previous attribute information.

Clause 70. The method of clause 65, wherein for the coordinate preprocessing for the RAHT, a point of a current processing unit and/or a point of a reference processing unit is inherited from a point used by a processed previous attribute information.

Clause 71. The method of any of clauses 36 to 70, wherein information of whether to and/or how to perform the process for the attribute coding for the processing unit associated with the PC sample is signaled from an encoder to a decoder in one of the followings: a bitstream, a frame, a tile, a slice, or an octree.

Clause 72. The method of any of clauses 36 to 70, further comprising: determining, based on coded information of the PC sample of the point cloud sequence, whether to and/or how to perform the process for the attribute coding for the processing unit associated with the PC sample, the coded information including at least one of: a dimension, a colour format, a colour component, a slice type, or a picture type.

Clause 73. A method for point cloud coding, comprising: generating, for a conversion between a point cloud (PC) sample of a point cloud sequence and a bitstream of the point cloud sequence, a reconstructed processing unit in a first processing unit level associated with the PC sample by adding a reconstructed processing unit in a second processing unit level, wherein attributes of a processing unit in the second processing unit level are reconstructed; and performing the conversion based on the reconstructed processing unit in the first processing unit level.

Clause 74. The method of clause 73, wherein a processing unit in the first processing unit level comprises at least one processing unit in the second processing unit level.

Clause 75. The method of clause 74, wherein the processing unit in the first processing unit level comprises at least one of a tile or a frame, and/or wherein the processing unit in the second processing unit level comprises a slice.

Clause 76. The method of any of clauses 73 to 75, wherein information of whether to and/or how to generate the reconstructed processing unit in the first processing unit level associated with the PC sample by adding the reconstructed processing unit in the second processing unit level is signaled from an encoder to a decoder in one of the followings: a bitstream, a frame, a tile, a slice, or an octree.

Clause 77. The method of any of clauses 73 to 75, further comprising: determining, based on coded information of the PC sample of the point cloud sequence, whether to and/or how to generate the reconstructed processing unit in the first processing unit level associated with the PC sample by adding the reconstructed processing unit in the second processing unit level, the coded information including at least one of: a dimension, a colour format, a colour component, a slice type, or a picture type.

Clause 78. The method of any of clauses 1-77, wherein the conversion includes encoding the PC sample into the bitstream.

Clause 79. The method of any of clauses 1-77, wherein the conversion includes decoding the PC sample from the bitstream.

Clause 80. An apparatus for processing point cloud data comprising a processor and a non-transitory memory with instructions thereon, wherein the instructions upon execution by the processor, cause the processor to perform a method in accordance with any of clauses 1-79.

Clause 81. A non-transitory computer-readable storage medium storing instructions that cause a processor to perform a method in accordance with any of clauses 1-79.

Clause 82. A non-transitory computer-readable recording medium storing a bitstream of a point cloud sequence which is generated by a method performed by a point cloud processing apparatus, wherein the method comprises: determining that a target process is performed for a set of attributes in a plurality of attributes of a processing unit associated with a point cloud (PC) sample of the point cloud sequence; and generating the bitstream based on the set of processed attributes.

Clause 83. A method for storing a bitstream of a point cloud sequence, comprising: determining that a target process is performed for a set of attributes in a plurality of attributes of a processing unit associated with a point cloud (PC) sample of the point cloud sequence; generating the bitstream based on the set of processed attributes; and storing the bitstream in a non-transitory computer-readable recording medium.

Clause 84. A non-transitory computer-readable recording medium storing a bitstream of a point cloud sequence which is generated by a method performed by a point cloud processing apparatus, wherein the method comprises: performing a process for an attribute coding for a processing unit associated with a point cloud (PC) sample of the point cloud sequence, wherein the process comprises at least one of: a generation of a level of a detail, a process of predictor search, a preprocessing of a geometry coordinate, a generation of a reference processing unit, or a coordinate preprocessing for a region-adaptive hierarchical transform (RAHT) ; and generating the bitstream based on the processed processing unit.

Clause 85. A method for storing a bitstream of a point cloud sequence, comprising: performing a process for an attribute coding for a processing unit associated with a point cloud (PC) sample of the point cloud sequence, wherein the process comprises at least one of: a generation of a level of a detail, a process of predictor search, a preprocessing of a geometry coordinate, a generation of a reference processing unit, or a coordinate preprocessing for a region-adaptive hierarchical transform (RAHT) ; generating the bitstream based on the processed processing unit; and storing the bitstream in a non-transitory computer-readable recording medium.

Clause 86. A non-transitory computer-readable recording medium storing a bitstream of a point cloud sequence which is generated by a method performed by a point cloud processing apparatus, wherein the method comprises: generating a reconstructed processing unit in a first processing unit level associated with a point cloud (PC) sample of the point cloud sequence by adding a reconstructed processing unit in a second processing unit level, wherein attributes of a processing unit in the second processing unit level are reconstructed; and generating the bitstream based on the reconstructed processing unit in the first processing unit level.

Clause 87. A method for storing a bitstream of a point cloud sequence, comprising: generating a reconstructed processing unit in a first processing unit level associated with a point cloud (PC) sample of the point cloud sequence by adding a reconstructed processing unit in a second processing unit level, wherein attributes of a processing unit in the second processing unit level are reconstructed; generating the bitstream based on the reconstructed processing unit in the first processing unit level; and storing the bitstream in a non-transitory computer-readable recording medium.
Example Device

Fig. 7 illustrates a block diagram of a computing device 700 in which various embodiments of the present disclosure can be implemented. The computing device 700 may be implemented as or included in the source device 110 (or the GPCC encoder 116 or 200) or the destination device 120 (or the GPCC decoder 126 or 300) .

It would be appreciated that the computing device 700 shown in Fig. 7 is merely for purpose of illustration, without suggesting any limitation to the functions and scopes of the embodiments of the present disclosure in any manner.

As shown in Fig. 7, the computing device 700 includes a general-purpose computing device 700. The computing device 700 may at least comprise one or more processors or processing units 710, a memory 720, a storage unit 730, one or more communication units 740, one or more input devices 750, and one or more output devices 760.

In some embodiments, the computing device 700 may be implemented as any user terminal or server terminal having the computing capability. The server terminal may be a server, a large-scale computing device or the like that is provided by a service provider. The user terminal may for example be any type of mobile terminal, fixed terminal, or portable terminal, including a mobile phone, station, unit, device, multimedia computer, multimedia tablet, Internet node, communicator, desktop computer, laptop computer, notebook computer, netbook computer, tablet computer, personal communication system (PCS) device, personal navigation device, personal digital assistant (PDA) , audio/video player, digital camera/video camera, positioning device, television receiver, radio broadcast receiver, E-book device, gaming device, or any combination thereof, including the accessories and peripherals of these devices, or any combination thereof. It would be contemplated that the computing device 700 can support any type of interface to a user (such as “wearable” circuitry and the like) .

The processing unit 710 may be a physical or virtual processor and can implement various processes based on programs stored in the memory 720. In a multi-processor system, multiple processing units execute computer executable instructions in parallel so as to improve the parallel processing capability of the computing device 700. The processing unit 710 may also be referred to as a central processing unit (CPU) , a microprocessor, a controller or a microcontroller.

The computing device 700 typically includes various computer storage medium. Such medium can be any medium accessible by the computing device 700, including, but not limited to, volatile and non-volatile medium, or detachable and non-detachable medium. The memory 720 can be a volatile memory (for example, a register, cache, Random Access Memory (RAM) ) , a non-volatile memory (such as a Read-Only Memory (ROM) , Electrically Erasable Programmable Read-Only Memory (EEPROM) , or a flash memory) , or any combination thereof. The storage unit 730 may be any detachable or non-detachable medium and may include a machine-readable medium such as a memory, flash memory drive, magnetic disk or another other media, which can be used for storing information and/or data and can be accessed in the computing device 700.

The computing device 700 may further include additional detachable/non-detachable, volatile/non-volatile memory medium. Although not shown in Fig. 7, it is possible to provide a magnetic disk drive for reading from and/or writing into a detachable and non-volatile magnetic disk and an optical disk drive for reading from and/or writing into a detachable non-volatile optical disk. In such cases, each drive may be connected to a bus (not shown) via one or more data medium interfaces.

The communication unit 740 communicates with a further computing device via the communication medium. In addition, the functions of the components in the computing device 700 can be implemented by a single computing cluster or multiple computing machines that can communicate via communication connections. Therefore, the computing device 700 can operate in a networked environment using a logical connection with one or more other servers, networked personal computers (PCs) or further general network nodes.

The input device 750 may be one or more of a variety of input devices, such as a mouse, keyboard, tracking ball, voice-input device, and the like. The output device 760 may be one or more of a variety of output devices, such as a display, loudspeaker, printer, and the like. By means of the communication unit 740, the computing device 700 can further communicate with one or more external devices (not shown) such as the storage devices and display device, with one or more devices enabling the user to interact with the computing device 700, or any devices (such as a network card, a modem and the like) enabling the computing device 700 to communicate with one or more other computing devices, if required. Such communication can be performed via input/output (I/O) interfaces (not shown) .

In some embodiments, instead of being integrated in a single device, some or all components of the computing device 700 may also be arranged in cloud computing architecture. In the cloud computing architecture, the components may be provided remotely and work together to implement the functionalities described in the present disclosure. In some embodiments, cloud computing provides computing, software, data access and storage service, which will not require end users to be aware of the physical locations or configurations of the systems or hardware providing these services. In various embodiments, the cloud computing provides the services via a wide area network (such as Internet) using suitable protocols. For example, a cloud computing provider provides applications over the wide area network, which can be accessed through a web browser or any other computing components. The software or components of the cloud computing architecture and corresponding data may be stored on a server at a remote position. The computing resources in the cloud computing environment may be merged or distributed at locations in a remote data center. Cloud computing infrastructures may provide the services through a shared data center, though they behave as a single access point for the users. Therefore, the cloud computing architectures may be used to provide the components and functionalities described herein from a service provider at a remote location. Alternatively, they may be provided from a conventional server or installed directly or otherwise on a client device.

The computing device 700 may be used to implement point cloud encoding/decoding in embodiments of the present disclosure. The memory 720 may include one or more point cloud coding modules 725 having one or more program instructions. These modules are accessible and executable by the processing unit 710 to perform the functionalities of the various embodiments described herein.

In the example embodiments of performing point cloud encoding, the input device 750 may receive point cloud data as an input 770 to be encoded. The point cloud data may be processed, for example, by the point cloud coding module 725, to generate an encoded bitstream. The encoded bitstream may be provided via the output device 760 as an output 780.

In the example embodiments of performing point cloud decoding, the input device 750 may receive an encoded bitstream as the input 770. The encoded bitstream may be processed, for example, by the point cloud coding module 725, to generate decoded point cloud data. The decoded point cloud data may be provided via the output device 760 as the output 780.

While this disclosure has been particularly shown and described with references to example embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the present application as defined by the appended claims. Such variations are intended to be covered by the scope of this present application. As such, the foregoing description of embodiments of the present application is not intended to be limiting.

Claims

A method for point cloud coding, comprising:

determining, for a conversion between a point cloud (PC) sample of a point cloud sequence and a bitstream of the point cloud sequence, that a target process is performed for a set of attributes in a plurality of attributes of a processing unit associated with the PC sample; and

performing the conversion based on the set of processed attributes.
The method of claim 1, wherein there is multiple attributes for one processing unit.
The method of claim 1, wherein target information is used for coding of an attribute of the processing unit.
The method of claim 3, wherein the target information comprises one or more coordinates used for the coding of the attribute of the processing unit.
The method of claim 3, wherein the target information comprises a level of details structure constructed for the coding of the attribute of the processing unit.
The method of claim 3, wherein the target information comprises at least one of a searched predictor or a character of the predictor used for the coding of the attribute of the processing unit.
The method of claim 6, wherein the character of the predictor comprises a predictor weight.
The method of claim 3, wherein the target information comprises one or more sorted points based on one or more coordinates used for the coding of the attribute of the processing unit.
The method of claim 3, wherein the target information comprises a region-adaptive hierarchical transform (RAHT) transform tree structure used for RAHT attribute coding.
The method of claim 3, wherein the target information comprises a reference processing unit used for an inter prediction of the attribute of the processing unit.
The method of claim 1, wherein the target process is used to generate target information for coding of an attribute of the processing unit.
The method of claim 1, wherein an input for the target process is used to generate target information for coding of an attribute of the processing unit.
The method of claim 12, wherein the input comprises a parameter to control the target process, or

wherein the input comprises a parameter to be used in the target process.
The method of claim 12, wherein the input comprises input information used to generate the target information.
The method of claim 14, wherein the input information comprises at least one of the following:

an original coordinate for attribute coding,

one or more original reference frames,

one or more unsorted points based on one or more coordinates, or

one or more points used for RAHT transform tree building.
The method of claim 1, wherein if the target process is determined to be enabled to be skipped for one or more attributes, the target process is performed for a target attribute of the processing unit.
The method of claim 1, wherein an approach is used to determine whether the target process is performed for an attribute of the processing unit.
The method of claim 17, wherein if there is no previously coded attribute for the processing unit, the target process is performed for a current attribute of the processing unit.
The method of claim 17, wherein if an input of the target process for a current attribute of the processing unit is different from an input of the target process for a previously coded attribute of the processing unit, the target process is performed for the current attribute of the processing unit.
The method of claim 1, wherein an approach is used to determine whether the target process is skipped for an attribute of the processing unit.
The method of claim 20, wherein if an input of the target process for a current attribute of the processing unit is same as an input of the target process for a previously coded attribute of the processing unit, the target process is skipped for the current attribute of the processing unit.
The method of claim 20, wherein if an input of the target process for a current attribute of the processing unit is partly same as an input of the target process for a previously coded attribute of the processing unit, the target process is skipped for the current attribute of the processing unit.
The method of claim 1, wherein the target process is performed for a target attribute of the processing unit, and the target process is skipped for another attribute of the processing unit.
The method of claim 23, wherein the target attribute is the first attribute of the processing unit in coding order.
The method of claim 23, wherein the other attribute inherits target information for attribute coding of the processing unit from the first attribute in coding order.
The method of claim 23, wherein the target attribute for which the target process is performed is derived at an encoder.
The method of claim 23, wherein the target attribute for which the target process is performed is derived at a decoder.
The method of claim 23, wherein the target attribute for which the target process is performed is signalled to a decoder.
The method of claim 23, wherein the other attribute for which the target process is skipped is signalled to a decoder.
The method of claim 23, wherein the other attribute for which the target process is skipped inherits target information for attribute coding of the processing unit from the target attribute for which the target process is performed.
The method of claim 30, wherein the target attribute which the other attribute inherits the target information from is derived at an encoder.
The method of claim 30, wherein the target attribute which the other attribute inherits the target information from is derived at a decoder.
The method of claim 30, wherein the target attribute which the other attribute inherits the target information from is signalled to an encoder.
The method of any of claims 1 to 33, wherein information of whether to and/or how to determine that the target process is performed for the set of attributes in the plurality of attributes of the processing unit is signaled from an encoder to a decoder in one of the followings:

a bitstream,

a frame,

a tile,

a slice, or

an octree.
The method of any of claims 1 to 33, further comprising:

determining, based on coded information of the PC sample of the point cloud sequence, whether to and/or how to determine that the target process is performed for the set of attributes in the plurality of attributes of the processing unit, the coded information including at least one of:

a dimension,

a colour format,

a colour component,

a slice type, or

a picture type.
A method for point cloud coding, comprising:

performing, for a conversion between a point cloud (PC) sample of a point cloud sequence and a bitstream of the point cloud sequence, a process for an attribute coding for a processing unit associated with the PC sample, wherein the process comprises at least one of: a generation of a level of a detail, a process of predictor search, a preprocessing of a geometry coordinate, a generation of a reference processing unit, or a coordinate preprocessing for a region-adaptive hierarchical transform (RAHT) ; and

performing the conversion based on the processed processing unit.
The method of claim 36, wherein if an inter prediction is used for the attribute coding, the generation of the level of the detail and/or the process of the predictor search is performed.
The method of claim 36, wherein if an angular coordinate is used for the attribute coding, the preprocessing of the geometry coordinate is performed.
The method of claim 36, wherein if an inter prediction is used for the attribute coding, the generation of the reference processing unit is performed.
The method of claim 36, wherein if a RAHT based approach is used for the attribute coding, the coordinate preprocessing for the RAHT is performed.
The method of claim 36, wherein the preprocessing of the geometry coordinate comprises a procedure for generating a coordinate required for the attribute coding.
The method of claim 41, wherein the preprocessing of the geometry coordinate comprises a coordinate conversion.
The method of claim 42, wherein the coordinate conversion comprises a coordinate conversion of a current processing unit.
The method of claim 42, wherein the coordinate conversion comprises a coordinate conversion of a reference processing unit.
The method of claim 41, wherein the preprocessing of the geometry coordinate comprises at least one of: a coordinate scaling or a coordinate shifting.
The method of claim 45, wherein the coordinate scaling and/or the coordinate shifting comprise at least one of: a coordinate scaling of a current processing unit, or a coordinate shifting of the current processing unit.
The method of claim 45, wherein the coordinate scaling and/or the coordinate shifting comprise at least one of: a coordinate scaling of a reference processing unit, or a coordinate shifting of the reference processing unit.
The method of claim 36, wherein the coordinate preprocessing for the RAHT comprises at least one of:a preprocessing of a point of a current processing unit required by the RAHT, or a preprocessing of a point of a reference processing unit required by the RAHT.
The method of claim 48, wherein the point of the current processing unit and/or the point of the reference processing unit comprise at least one of a coordinate or a corresponding attribute.
The method of claim 48, wherein the coordinate preprocessing for the RAHT comprises a Morton code generation of a coordinate.
The method of claim 50, wherein the Morton code generation of coordinate comprises a Morton code generation of coordinate for the current processing unit.
The method of claim 50, wherein the Morton code generation of coordinate comprises a Morton code generation of coordinate for the reference processing unit.
The method of claim 48, wherein the coordinate preprocessing for the RAHT comprises a reordering of one or more points.
The method of claim 53, wherein the reordering of the one or more point comprises a reordering of one or more points for the current processing unit.
The method of claim 53, wherein the reordering of the one or more point comprises a reordering of one or more points for the reference processing unit.
The method of claim 53, wherein the reordering is based on a Morton code of the point.
The method of claim 48, wherein the coordinate preprocessing for the RAHT comprises a relabeling of an attribute.
The method of claim 57, wherein the relabeling of the attribute comprises a relabeling of an attribute for the current processing unit.
The method of claim 57, wherein the relabeling of the attribute comprises a relabeling of an attribute for the reference processing unit.
The method of claim 57, wherein the relabeling of the attribute is based on at least one of a coordinate or a region structure.
The method of claim 36, wherein an indicator is used to indicate whether a current attribute is the first attribute of the processing unit.
The method of claim 61, wherein the indicator is signalled and/or derived at an encoder.
The method of claim 62, wherein the indicator is parsed or derived at a decoder.
The method of claim 36, wherein if a current attribute is the first attribute information of a current processing unit that needs to be processed, the process is performed.
The method of claim 36, wherein if a current attribute is not the first attribute information of a current processing unit that needs to be processed, the process is skipped.
The method of claim 65, wherein for the generation of the level of the detail and/or the process of the predictor search, the level of the detail and/or the searched predictor is inherited from a level of a detail and/or a searched predictor used by a processed previous attribute information.
The method of claim 65, wherein for the generation of the level of the detail and/or the process of the predictor search, a character of the searched predictor is inherited from a searched predictor used by a processed previous attribute information.
The method of claim 65, wherein for the preprocessing of the geometry coordinate, a coordinate required for a current attribute coding is inherited from a coordinate used by a processed previous attribute information.
The method of claim 65, wherein for the generation of the reference processing unit, a reference processing unit for a current attribute coding is inherited from a reference processing unit used by a processed previous attribute information.
The method of claim 65, wherein for the coordinate preprocessing for the RAHT, a point of a current processing unit and/or a point of a reference processing unit is inherited from a point used by a processed previous attribute information.
The method of any of claims 36 to 70, wherein information of whether to and/or how to perform the process for the attribute coding for the processing unit associated with the PC sample is signaled from an encoder to a decoder in one of the followings:

a bitstream,

a frame,

a tile,

a slice, or

an octree.
The method of any of claims 36 to 70, further comprising:

determining, based on coded information of the PC sample of the point cloud sequence, whether to and/or how to perform the process for the attribute coding for the processing unit associated with the PC sample, the coded information including at least one of:

a dimension,

a colour format,

a colour component,

a slice type, or

a picture type.
A method for point cloud coding, comprising:

generating, for a conversion between a point cloud (PC) sample of a point cloud sequence and a bitstream of the point cloud sequence, a reconstructed processing unit in a first processing unit level associated with the PC sample by adding a reconstructed processing unit in a second processing unit level, wherein attributes of a processing unit in the second processing unit level are reconstructed; and

performing the conversion based on the reconstructed processing unit in the first processing unit level.
The method of claim 73, wherein a processing unit in the first processing unit level comprises at least one processing unit in the second processing unit level.
The method of claim 74, wherein the processing unit in the first processing unit level comprises at least one of a tile or a frame, and/or

wherein the processing unit in the second processing unit level comprises a slice.
The method of any of claims 73 to 75, wherein information of whether to and/or how to generate the reconstructed processing unit in the first processing unit level associated with the PC sample by adding the reconstructed processing unit in the second processing unit level is signaled from an encoder to a decoder in one of the followings:

a bitstream,

a frame,

a tile,

a slice, or

an octree.
The method of any of claims 73 to 75, further comprising:

determining, based on coded information of the PC sample of the point cloud sequence, whether to and/or how to generate the reconstructed processing unit in the first processing unit level associated with the PC sample by adding the reconstructed processing unit in the second processing unit level, the coded information including at least one of:

a dimension,

a colour format,

a colour component,

a slice type, or

a picture type.
The method of any of claims 1-77, wherein the conversion includes encoding the PC sample into the bitstream.
The method of any of claims 1-77, wherein the conversion includes decoding the PC sample from the bitstream.
An apparatus for processing point cloud data comprising a processor and a non-transitory memory with instructions thereon, wherein the instructions upon execution by the processor, cause the processor to perform a method in accordance with any of claims 1-79.
A non-transitory computer-readable storage medium storing instructions that cause a processor to perform a method in accordance with any of claims 1-79.
A non-transitory computer-readable recording medium storing a bitstream of a point cloud sequence which is generated by a method performed by a point cloud processing apparatus, wherein the method comprises:

determining that a target process is performed for a set of attributes in a plurality of attributes of a processing unit associated with a point cloud (PC) sample of the point cloud sequence; and

generating the bitstream based on the set of processed attributes.
A method for storing a bitstream of a point cloud sequence, comprising:

determining that a target process is performed for a set of attributes in a plurality of attributes of a processing unit associated with a point cloud (PC) sample of the point cloud sequence;

generating the bitstream based on the set of processed attributes; and

storing the bitstream in a non-transitory computer-readable recording medium.
A non-transitory computer-readable recording medium storing a bitstream of a point cloud sequence which is generated by a method performed by a point cloud processing apparatus, wherein the method comprises:

performing a process for an attribute coding for a processing unit associated with a point cloud (PC) sample of the point cloud sequence, wherein the process comprises at least one of: a generation of a level of a detail, a process of predictor search, a preprocessing of a geometry coordinate, a generation of a reference processing unit, or a coordinate preprocessing for a region-adaptive hierarchical transform (RAHT) ; and

generating the bitstream based on the processed processing unit.
A method for storing a bitstream of a point cloud sequence, comprising:

performing a process for an attribute coding for a processing unit associated with a point cloud (PC) sample of the point cloud sequence, wherein the process comprises at least one of: a generation of a level of a detail, a process of predictor search, a preprocessing of a geometry coordinate, a generation of a reference processing unit, or a coordinate preprocessing for a region-adaptive hierarchical transform (RAHT) ;

generating the bitstream based on the processed processing unit; and

storing the bitstream in a non-transitory computer-readable recording medium.
A non-transitory computer-readable recording medium storing a bitstream of a point cloud sequence which is generated by a method performed by a point cloud processing apparatus, wherein the method comprises:

generating a reconstructed processing unit in a first processing unit level associated with a point cloud (PC) sample of the point cloud sequence by adding a reconstructed processing unit in a second processing unit level, wherein attributes of a processing unit in the second processing unit level are reconstructed; and

generating the bitstream based on the reconstructed processing unit in the first processing unit level.
A method for storing a bitstream of a point cloud sequence, comprising:

generating a reconstructed processing unit in a first processing unit level associated with a point cloud (PC) sample of the point cloud sequence by adding a reconstructed processing unit in a second processing unit level, wherein attributes of a processing unit in the second processing unit level are reconstructed;

generating the bitstream based on the reconstructed processing unit in the first processing unit level; and

storing the bitstream in a non-transitory computer-readable recording medium.