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WO2025101793A1 - Mesure de géométrie adaptative pour des nuages de points 3d - Google Patents

Mesure de géométrie adaptative pour des nuages de points 3d Download PDF

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Publication number
WO2025101793A1
WO2025101793A1 PCT/US2024/054981 US2024054981W WO2025101793A1 WO 2025101793 A1 WO2025101793 A1 WO 2025101793A1 US 2024054981 W US2024054981 W US 2024054981W WO 2025101793 A1 WO2025101793 A1 WO 2025101793A1
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WIPO (PCT)
Prior art keywords
point cloud
point
distance
reference point
determining
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English (en)
Inventor
Jiahao PANG
Ritwik SADHU
Dong Tian
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InterDigital VC Holdings Inc
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InterDigital VC Holdings Inc
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Publication of WO2025101793A1 publication Critical patent/WO2025101793A1/fr
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/50—Depth or shape recovery
    • G06T7/521—Depth or shape recovery from laser ranging, e.g. using interferometry; from the projection of structured light
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10028—Range image; Depth image; 3D point clouds
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20076—Probabilistic image processing
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30248—Vehicle exterior or interior
    • G06T2207/30252—Vehicle exterior; Vicinity of vehicle

Definitions

  • Provisional Patent Application Serial No.63/297,869 entitled “A Scalable Framework for Point Cloud Compression” and filed Jan. 10, 2022 (“‘869 application”); U.S. Provisional Patent Application Serial No.63/388,087, entitled “A Scalable Framework for Point Cloud Compression” and filed July 11, 2022 (“‘087 application”); U.S. Provisional Patent Application Serial No.63/252,482, entitled “Method and Apparatus for Point Cloud Compression Using Hybrid Deep Entropy Coding” and filed October 5, 2021 (“‘482 application”); U.S.
  • Provisional Patent Application Serial No.63/297,894 entitled “Coordinate Refinement and Upsampling from Quantized Point Cloud Reconstruction” and filed January 10, 2022 (“‘894 application”);
  • U.S. Provisional Patent Application Serial No.63/388,600 entitled “Deep Distribution-Aware Point Feature Extractor for AI-Based Point Cloud Compression” and filed July 12, 2022 (“‘600 application”);
  • U.S. Provisional Patent Application Serial No.63/438,212 entitled “CONTEXT-AWARE VOXEL- BASED UPSAMPLING FOR POINT CLOUD PROCESSING” and filed January 10, 2023 (“‘212 application”); and
  • Point Cloud (PC) data format is a universal data format across several business domains, e.g., from autonomous driving, robotics, augmented reality/virtual reality (AR/VR), civil engineering, computer graphics, to the animation/movie industry.3D LiDAR (Light Detection and Ranging) sensors have been deployed in self- driving cars, and affordable LiDAR sensors are released from Velodyne Velabit, Apple iPad Pro 2020 and Intel RealSense LiDAR camera L515.
  • a first example method in accordance with some embodiments may include: accessing, for each point in a reference point cloud, a local covariance; accessing, for each point in the reference point cloud, a plurality of nearest neighboring points in a test point cloud; determining, for each point in the reference point cloud, a distance set, wherein the distance set includes Mahalanobis distances between a first point in the reference point cloud and a second point of the plurality of nearest neighboring points in the test point cloud; determining, for each point in the reference point cloud, a first distance by aggregating the distance set associated with each point in the reference point cloud; and determining a second distance between the test point cloud and the reference point cloud, by aggregating each of the first distances associated with each respective point of the reference point cloud.
  • aggregating the distance set associated with each point in the reference point cloud comprises averaging the distance set associated with each point in the reference cloud.
  • aggregating each of the first distances associated with each respective point of the reference point cloud comprises determining a maximum of the first distances associated with each respective point of the reference point cloud.
  • aggregating each of the first distances associated with each respective point of the reference point cloud comprises averaging each of the first distances associated with each respective point in the reference point cloud.
  • accessing the local covariance includes one of determining the local covariance and accessing a predetermined value of the local covariance.
  • a second example method in accordance with some embodiments may include: performing, for each point in a reference point cloud: obtaining a local covariance for the point in the reference point cloud; determining a distance set for the point in the reference point cloud, wherein the distance set includes Mahalanobis distances between the point in the reference point cloud and each of a plurality of nearest neighboring points in a test point cloud; and determining a first distance corresponding to the point in the reference point cloud by aggregating the distance set associated with the point in the reference point cloud; and determining a second distance between the test point cloud and the reference point cloud, by aggregating each of the first distances associated with each respective point of the reference point cloud.
  • aggregating the distance set associated with the point in the reference point cloud comprises determining a maximum of the distance set associated with the point in the reference point cloud. [0012] For some embodiments of the second example method, aggregating the distance set associated with the point in the reference point cloud comprises averaging the distance set associated with the point in the reference point cloud. [0013] For some embodiments of the second example method, averaging the distance set associated with the point in the reference point cloud comprises averaging the Mahalanobis distances with the distance set associated with the point in the reference point cloud.
  • aggregating each of the first distances associated with each respective point of the reference point cloud comprises determining a maximum of the first distances associated with each respective point of the reference point cloud. [0015] For some embodiments of the second example method, aggregating each of the first distances associated with each respective point of the reference point cloud comprises averaging each of the first distances associated with each respective point in the reference point cloud. [0016] For some embodiments of the second example method, obtaining the local covariance includes determining the local covariance.
  • the local covariance for the point in the reference point cloud includes a covariance matrix comprising distances between the point in the reference point cloud and each of a plurality of nearest neighboring points in the reference point cloud.
  • a third example method in accordance with some embodiments may include: performing a process loop for each point in a reference point cloud, wherein the process loop includes: selecting a first point equal to the respective point in the reference point cloud for a current pass through the process loop; obtaining a local covariance; determining a distance set, wherein the distance set includes Mahalanobis distances between the first point and each of the plurality of nearest neighboring points in a test point cloud; and determining a first distance by aggregating the distance set associated with each point in the reference point cloud; and determining a second distance between the test point cloud and the reference point cloud, by aggregating each of the first distances associated with each respective point of the reference point cloud.
  • aggregating the distance set associated with each point in the reference point cloud comprises determining a maximum of the distance set associated with each point in the reference point cloud. [0020] For some embodiments of the third example method, aggregating the distance set associated with each point in the reference point cloud comprises averaging the distance set associated with each point in the reference point cloud. [0021] For some embodiments of the third example method, aggregating each of the first distances associated with each respective point of the reference point cloud comprises averaging each of the first distances associated with each respective point in the reference point cloud.
  • performing the process loop for each point in the reference point cloud is performed at least partially in parallel for one or more points in the reference point cloud at the same time.
  • performing the process loop for each point in the reference point cloud is performed serially.
  • obtaining the local covariance includes determining the local covariance.
  • the local covariance for the point in the reference point cloud includes a covariance matrix comprising distances between the point in the reference point cloud and each of a plurality of nearest neighboring points in the reference point cloud.
  • determining the local covariance includes using a covariance matrix corresponding to the plurality of nearest neighbors of the first point. [0027] 1 For some embodiments of the third example method, determining the covariance matrix includes using an average of the plurality of nearest neighbors of the first point. [0028] For some embodiments of the third example method, determining the covariance matrix includes determining a series of differences between the first point and each of the plurality of nearest neighbors of the first point. [0029] For some embodiments of the third example method, determining the local covariance includes adding a small offset to one or more elements of the covariance matrix.
  • Some embodiments of the third example method may further include using the second distance as a loss function to train a deep neural network, wherein the covariance matrix is determined before training the deep neural network. [0031] Some embodiments of the third example method may further include changing the second distance dynamically during training of the deep neural network. [0032] For some embodiments of the third example method, changing the second distance dynamically includes adjusting how many nearest neighboring points are in the test point cloud. [0033] For some embodiments of the third example method, changing the second distance dynamically includes decreasing how many nearest neighboring points are in the test point cloud.
  • determining the local covariance includes using an inverse of a covariance matrix corresponding to the plurality of nearest neighbors of the first point.
  • Some embodiments of the third example method may further include: determining a second local covariance based on the reference point cloud; determining a third distance based on the second local covariance; and adding the third distance to the second distance to obtain an enhanced distance.
  • a third example apparatus in accordance with some embodiments may include: a processor; and a non-transitory computer-readable medium storing instructions operative, when executed by the processor, to cause the apparatus to: perform a process loop for each point in a reference point cloud, wherein the process loop includes: selecting a first point equal to the respective point in the reference point cloud for a current pass through the process loop; obtaining a local covariance; determining a distance set, wherein the distance set includes Mahalanobis distances between the first point and each of the plurality of nearest neighboring points in a test point cloud; and determine a first distance by aggregating the distance set associated with each point in the reference point cloud; and determine a second distance between the test point cloud and the reference point cloud, by aggregating each of the first distances associated with each respective point of the reference point cloud.
  • a fourth example method in accordance with some embodiments may include: performing a process loop for each point in a reference point cloud, wherein the process loop includes: selecting a first point equal to the respective point in the reference point cloud for a current pass through the process loop; obtaining a local covariance; determining a distance set, wherein the distance set includes square roots of Mahalanobis distances between the first point and each of the plurality of nearest neighboring points in a test point cloud; and determining a first distance by aggregating the distance set associated with each point in the reference point cloud; and determining a second distance between the test point cloud and the reference point cloud, by aggregating each of the first distances associated with each respective point of the reference point cloud.
  • aggregating the distance set associated with each point in the reference point cloud includes determining a maximum of the distance set associated with each point in the reference point cloud. [0039] For some embodiments of the fourth example method, aggregating the distance set associated with each point in the reference point cloud includes averaging the distance set associated with each point in the reference point cloud. [0040] For some embodiments of the fourth example method, averaging the distance set associated with each point in the reference point cloud includes averaging over the plurality of nearest neighboring points in the test point cloud. [0041] For some embodiments of the fourth example method, determining the local covariance includes using a covariance matrix corresponding to the plurality of nearest neighbors of the first point.
  • determining the covariance matrix includes using an average of the plurality of nearest neighbors of the first point. [0043] For some embodiments of the fourth example method, determining the covariance matrix includes determining a series of differences between the first point and each of the plurality of nearest neighbors of the first point. [0044] For some embodiments of the fourth example method, determining the local covariance includes adding a small offset to one or more elements of the covariance matrix. [0045] Some embodiments of the fourth example method may further include using the second distance as a loss function to train a deep neural network, wherein the covariance matrix is determined before training the deep neural network.
  • Some embodiments of the fourth example method may further include changing the second distance dynamically during training of the deep neural network.
  • changing the second distance dynamically includes adjusting how many nearest neighboring points are in the test point cloud.
  • changing the second distance dynamically includes decreasing how many nearest neighboring points are in the test point cloud.
  • determining the local covariance includes using an inverse of a covariance matrix corresponding to the plurality of nearest neighbors of the first point.
  • Some embodiments of the fourth example method may further include: determining a second local covariance based on the reference point cloud; determining a third distance based on the second local covariance; and adding the third distance to the second distance to obtain an enhanced distance.
  • a fourth example apparatus in accordance with some embodiments may include: a processor; and a non-transitory computer-readable medium storing instructions operative, when executed by the processor, to cause the apparatus to: perform a process loop for each point in a reference point cloud, wherein the process loop includes: selecting a first point equal to the respective point in the reference point cloud for a current pass through the process loop; obtaining a local covariance; determining a distance set, wherein the distance set includes square roots of Mahalanobis distances between the first point and each of the plurality of nearest neighboring points in a test point cloud; and determine a first distance by aggregating the distance set associated with each point in the reference point cloud; and determine a second distance between the test point cloud and the reference point cloud, by aggregating each of the first distances associated with each respective point of the reference point cloud.
  • a fifth example method in accordance with some embodiments may include: accessing, for each point in a first point cloud, a local covariance; accessing, for each point in the first point cloud, a plurality of nearest neighboring points in a second point cloud; determining, for each point in the first point cloud, a distance set, wherein the distance set comprises Mahalanobis distances between a first point in the first point cloud and a second point of the plurality of nearest neighboring points in the second point cloud; determining, for each point in the first point cloud, a first distance by aggregating the distance set associated with each point in the first point cloud; and determining a second distance between the second point cloud and the first point cloud, by aggregating each of the first distances associated with each respective point of the first point cloud.
  • a fifth example apparatus in accordance with some embodiments may include: a processor; and a non-transitory computer-readable medium storing instructions operative, when executed by the processor, to cause the apparatus to: accessing, for each point in a first point cloud, a local covariance; accessing, for each point in the first point cloud, a plurality of nearest neighboring points in a second point cloud; determining, for each point in the first point cloud, a distance set, wherein the distance set comprises Mahalanobis distances between a first point in the first point cloud and a second point of the plurality of nearest neighboring points in the second point cloud; determining, for each point in the first point cloud, a first distance by aggregating the distance set associated with each point in the first point cloud; and determining a second distance between the second point cloud and the first point cloud, by aggregating each of the first distances associated with each respective point of the first point cloud.
  • a sixth example apparatus in accordance with some embodiments may include at least one processor configured to perform any one of the above methods.
  • a seventh example apparatus in accordance with some embodiments may include a computer- readable medium storing instructions for causing one or more processors to perform any one of the above methods.
  • a eighth example apparatus in accordance with some embodiments may include at least one processor and at least one non-transitory computer-readable medium storing instructions for causing the at least one processor to perform any one of the above methods.
  • An example signal in accordance with some embodiments may include a bitstream generated according to any one of the above methods.
  • encoder and decoder apparatus are provided to perform the methods described herein.
  • An encoder or decoder apparatus may include a processor configured to perform the methods described herein.
  • the apparatus may include a computer-readable medium (e.g. a non-transitory medium) storing instructions for performing the methods described herein.
  • a computer-readable medium e.g. a non-transitory medium stores a video encoded using any of the methods described herein.
  • One or more of the present embodiments also provide a computer readable storage medium having stored thereon instructions for performing bi-directional optical flow, encoding or decoding video data according to any of the methods described above.
  • the present embodiments also provide a computer readable storage medium having stored thereon a bitstream generated according to the methods described above.
  • FIG. 1A is a system diagram illustrating an example communications system according to some embodiments.
  • FIG.1B is a system diagram illustrating an example wireless transmit/receive unit (WTRU) that may be used within the communications system illustrated in FIG.1A according to some embodiments.
  • WTRU wireless transmit/receive unit
  • FIG.1C is a system diagram illustrating an example set of interfaces for a system according to some embodiments.
  • FIG.2A is a schematic side view illustrating an example waveguide display that may be used with extended reality (XR) applications according to some embodiments.
  • FIG.2B is a schematic side view illustrating an example alternative display type that may be used with extended reality applications according to some embodiments.
  • FIG.2C is a schematic side view illustrating an example alternative display type that may be used with extended reality applications according to some embodiments.
  • FIG.3 is a schematic illustration showing an example estimation of a covariance matrix for a reference point cloud according to some embodiments.
  • FIG.4 is a schematic illustration showing an example set of isolines of a Mahalanobis distance metric according to some embodiments.
  • FIG.5 is a flowchart illustrating an example process for determining an LMD metric according to some embodiments.
  • FIG.6 is a flowchart illustrating an example process for dynamic training of a deep neural network with an LMD according to some embodiments.
  • FIG.7 is a flowchart illustrating an example process for determining a Local Mahalanobis Distance (LMD) metric according to some embodiments.
  • LMD Local Mahalanobis Distance
  • FIG. 1A is a diagram illustrating an example communications system 100 in which one or more disclosed embodiments may be implemented.
  • the communications system 100 may be a multiple access system that provides content, such as voice, data, video, messaging, broadcast, etc., to multiple wireless users.
  • the communications system 100 may enable multiple wireless users to access such content through the sharing of system resources, including wireless bandwidth.
  • the communications systems 100 may employ one or more channel access methods, such as code division multiple access (CDMA), time division multiple access (TDMA), frequency division multiple access (FDMA), orthogonal FDMA (OFDMA), single-carrier FDMA (SC-FDMA), zero-tail unique-word DFT-Spread OFDM (ZT UW DTS-s OFDM), unique word OFDM (UW-OFDM), resource block-filtered OFDM, filter bank multicarrier (FBMC), and the like.
  • CDMA code division multiple access
  • TDMA time division multiple access
  • FDMA frequency division multiple access
  • OFDMA orthogonal FDMA
  • SC-FDMA single-carrier FDMA
  • ZT UW DTS-s OFDM unique word OFDM
  • the communications system 100 may include wireless transmit/receive units (WTRUs) 102a, 102b, 102c, 102d, a RAN 104/113, a CN 106, a public switched telephone network (PSTN) 108, the Internet 110, and other networks 112, though it will be appreciated that the disclosed embodiments contemplate any number of WTRUs, base stations, networks, and/or network elements.
  • WTRUs 102a, 102b, 102c, 102d may be any type of device configured to operate and/or communicate in a wireless environment.
  • the WTRUs 102a, 102b, 102c, 102d may be configured to transmit and/or receive wireless signals and may include a user equipment (UE), a mobile station, a fixed or mobile subscriber unit, a subscription-based unit, a pager, a cellular telephone, a personal digital assistant (PDA), a smartphone, a laptop, a netbook, a personal computer, a wireless sensor, a hotspot or Mi-Fi device, an Internet of Things (IoT) device, a watch or other wearable, a head-mounted display (HMD), a vehicle, a drone, a medical device and applications (e.g., remote surgery), an industrial device and applications (e.g., a robot and/or other wireless devices operating in an industrial and/or an automated processing chain contexts), a consumer electronics device, a device operating on commercial and/or industrial wireless networks, and the like.
  • UE user equipment
  • PDA personal digital assistant
  • smartphone a laptop
  • a netbook a personal computer
  • the communications systems 100 may also include a base station 114a and/or a base station 114b.
  • Each of the base stations 114a, 114b may be any type of device configured to wirelessly interface with at least one of the WTRUs 102a, 102b, 102c, 102d to facilitate access to one or more communication networks, such as the CN 106, the Internet 110, and/or the other networks 112.
  • the base stations 114a, 114b may be a base transceiver station (BTS), a Node-B, an eNode B, a Home Node B, a Home eNode B, a gNB, a NR NodeB, a site controller, an access point (AP), a wireless router, and the like. While the base stations 114a, 114b are each depicted as a single element, it will be appreciated that the base stations 114a, 114b may include any number of interconnected base stations and/or network elements.
  • the base station 114a may be part of the RAN 104/113, which may also include other base stations and/or network elements (not shown), such as a base station controller (BSC), a radio network controller (RNC), relay nodes, etc.
  • BSC base station controller
  • RNC radio network controller
  • the base station 114a and/or the base station 114b may be configured to transmit and/or receive wireless signals on one or more carrier frequencies, which may be referred to as a cell (not shown). These frequencies may be in licensed spectrum, unlicensed spectrum, or a combination of licensed and unlicensed spectrum.
  • a cell may provide coverage for a wireless service to a specific geographical area that may be relatively fixed or that may change over time. The cell may further be divided into cell sectors.
  • the cell associated with the base station 114a may be divided into three sectors.
  • the base station 114a may include three transceivers, i.e., one for each sector of the cell.
  • the base station 114a may employ multiple-input multiple output (MIMO) technology and may utilize multiple transceivers for each sector of the cell.
  • MIMO multiple-input multiple output
  • beamforming may be used to transmit and/or receive signals in desired spatial directions.
  • the base stations 114a, 114b may communicate with one or more of the WTRUs 102a, 102b, 102c, 102d over an air interface 116, which may be any suitable wireless communication link (e.g., radio frequency (RF), microwave, centimeter wave, micrometer wave, infrared (IR), ultraviolet (UV), visible light, etc.).
  • the air interface 116 may be established using any suitable radio access technology (RAT).
  • RAT radio access technology
  • the communications system 100 may be a multiple access system and may employ one or more channel access schemes, such as CDMA, TDMA, FDMA, OFDMA, SC-FDMA, and the like.
  • the base station 114a in the RAN 104/113 and the WTRUs 102a, 102b, 102c may implement a radio technology such as Universal Mobile Telecommunications System (UMTS) Terrestrial Radio Access (UTRA), which may establish the air interface 116 using wideband CDMA (WCDMA).
  • WCDMA may include communication protocols such as High-Speed Packet Access (HSPA) and/or Evolved HSPA (HSPA+).
  • HSPA may include High-Speed Downlink (DL) Packet Access (HSDPA) and/or High-Speed UL Packet Access (HSUPA).
  • the base station 114a and the WTRUs 102a, 102b, 102c may implement a radio technology such as Evolved UMTS Terrestrial Radio Access (E-UTRA), which may establish the air interface 116 using Long Term Evolution (LTE) and/or LTE-Advanced (LTE-A) and/or LTE-Advanced Pro (LTE-A Pro).
  • E-UTRA Evolved UMTS Terrestrial Radio Access
  • LTE Long Term Evolution
  • LTE-A LTE-Advanced
  • LTE-A Pro LTE-Advanced Pro
  • the base station 114a and the WTRUs 102a, 102b, 102c may implement a radio technology such as NR Radio Access , which may establish the air interface 116 using New Radio (NR).
  • NR New Radio
  • the base station 114a and the WTRUs 102a, 102b, 102c may implement multiple radio access technologies.
  • the base station 114a and the WTRUs 102a, 102b, 102c may implement LTE radio access and NR radio access together, for instance using dual connectivity (DC) principles.
  • DC dual connectivity
  • the air interface utilized by WTRUs 102a, 102b, 102c may be characterized by multiple types of radio access technologies and/or transmissions sent to/from multiple types of base stations (e.g., a eNB and a gNB).
  • the base station 114a and the WTRUs 102a, 102b, 102c may implement radio technologies such as IEEE 802.11 (i.e., Wireless Fidelity (WiFi), IEEE 802.16 (i.e., Worldwide Interoperability for Microwave Access (WiMAX)), CDMA2000, CDMA20001X, CDMA2000 EV-DO, Interim Standard 2000 (IS- 2000), Interim Standard 95 (IS-95), Interim Standard 856 (IS-856), Global System for Mobile communications (GSM), Enhanced Data rates for GSM Evolution (EDGE), GSM EDGE (GERAN), and the like.
  • IEEE 802.11 i.e., Wireless Fidelity (WiFi)
  • IEEE 802.16 i.e., Worldwide Interoperability for Microwave Access (WiMAX)
  • CDMA2000, CDMA20001X, CDMA2000 EV-DO Code Division Multiple Access 2000
  • IS- 2000 Interim Standard 95
  • IS-856 Interim Standard 856
  • GSM Global System for
  • the base station 114b in FIG.1A may be a wireless router, Home Node B, Home eNode B, or access point, for example, and may utilize any suitable RAT for facilitating wireless connectivity in a localized area, such as a place of business, a home, a vehicle, a campus, an industrial facility, an air corridor (e.g., for use by drones), a roadway, and the like.
  • the base station 114b and the WTRUs 102c, 102d may implement a radio technology such as IEEE 802.11 to establish a wireless local area network (WLAN).
  • WLAN wireless local area network
  • the base station 114b and the WTRUs 102c, 102d may implement a radio technology such as IEEE 802.15 to establish a wireless personal area network (WPAN).
  • the base station 114b and the WTRUs 102c, 102d may utilize a cellular-based RAT (e.g., WCDMA, CDMA2000, GSM, LTE, LTE-A, LTE-A Pro, NR etc.) to establish a picocell or femtocell.
  • the base station 114b may have a direct connection to the Internet 110.
  • the base station 114b may not be required to access the Internet 110 via the CN 106.
  • the RAN 104/113 may be in communication with the CN 106, which may be any type of network configured to provide voice, data, applications, and/or voice over internet protocol (VoIP) services to one or more of the WTRUs 102a, 102b, 102c, 102d.
  • the data may have varying quality of service (QoS) requirements, such as differing throughput requirements, latency requirements, error tolerance requirements, reliability requirements, data throughput requirements, mobility requirements, and the like.
  • QoS quality of service
  • the CN 106 may provide call control, billing services, mobile location-based services, pre-paid calling, Internet connectivity, video distribution, etc., and/or perform high-level security functions, such as user authentication.
  • the RAN 104/113 and/or the CN 106 may be in direct or indirect communication with other RANs that employ the same RAT as the RAN 104/113 or a different RAT.
  • the CN 106 may also be in communication with another RAN (not shown) employing a GSM, UMTS, CDMA 2000, WiMAX, E-UTRA, or WiFi radio technology.
  • the CN 106 may also serve as a gateway for the WTRUs 102a, 102b, 102c, 102d to access the PSTN 108, the Internet 110, and/or the other networks 112.
  • the PSTN 108 may include circuit-switched telephone networks that provide plain old telephone service (POTS).
  • POTS plain old telephone service
  • the Internet 110 may include a global system of interconnected computer networks and devices that use common communication protocols, such as the transmission control protocol (TCP), user datagram protocol (UDP) and/or the internet protocol (IP) in the TCP/IP internet protocol suite.
  • the networks 112 may include wired and/or wireless communications networks owned and/or operated by other service providers.
  • the networks 112 may include another CN connected to one or more RANs, which may employ the same RAT as the RAN 104/113 or a different RAT.
  • Some or all of the WTRUs 102a, 102b, 102c, 102d in the communications system 100 may include multi-mode capabilities (e.g., the WTRUs 102a, 102b, 102c, 102d may include multiple transceivers for communicating with different wireless networks over different wireless links).
  • the WTRU 102c shown in FIG.1A may be configured to communicate with the base station 114a, which may employ a cellular- based radio technology, and with the base station 114b, which may employ an IEEE 802 radio technology.
  • FIG.1B is a system diagram illustrating an example WTRU 102.
  • the WTRU 102 may include a processor 118, a transceiver 120, a transmit/receive element 122, a speaker/microphone 124, a keypad 126, a display/touchpad 128, non-removable memory 130, removable memory 132, a power source 134, a global positioning system (GPS) chipset 136, and/or other peripherals 138, among others.
  • GPS global positioning system
  • the processor 118 may be a general purpose processor, a special purpose processor, a conventional processor, a digital signal processor (DSP), a plurality of microprocessors, one or more microprocessors in association with a DSP core, a controller, a microcontroller, Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) circuits, any other type of integrated circuit (IC), a state machine, and the like.
  • the processor 118 may perform signal coding, data processing, power control, input/output processing, and/or any other functionality that enables the WTRU 102 to operate in a wireless environment.
  • the processor 118 may be coupled to the transceiver 120, which may be coupled to the transmit/receive element 122.
  • the transmit/receive element 122 may be configured to transmit signals to, or receive signals from, a base station (e.g., the base station 114a) over the air interface 116.
  • a base station e.g., the base station 114a
  • the transmit/receive element 122 may be an antenna configured to transmit and/or receive RF signals.
  • the transmit/receive element 122 may be an emitter/detector configured to transmit and/or receive IR, UV, or visible light signals, for example.
  • the transmit/receive element 122 may be configured to transmit and/or receive both RF and light signals. It will be appreciated that the transmit/receive element 122 may be configured to transmit and/or receive any combination of wireless signals.
  • the transmit/receive element 122 is depicted in FIG.1B as a single element, the WTRU 102 may include any number of transmit/receive elements 122. More specifically, the WTRU 102 may employ MIMO technology. Thus, in one embodiment, the WTRU 102 may include two or more transmit/receive elements 122 (e.g., multiple antennas) for transmitting and receiving wireless signals over the air interface 116.
  • the transceiver 120 may be configured to modulate the signals that are to be transmitted by the transmit/receive element 122 and to demodulate the signals that are received by the transmit/receive element 122.
  • the WTRU 102 may have multi-mode capabilities.
  • the transceiver 120 may include multiple transceivers for enabling the WTRU 102 to communicate via multiple RATs, such as NR and IEEE 802.11, for example.
  • the processor 118 of the WTRU 102 may be coupled to, and may receive user input data from, the speaker/microphone 124, the keypad 126, and/or the display/touchpad 128 (e.g., a liquid crystal display (LCD) display unit or organic light-emitting diode (OLED) display unit).
  • the processor 118 may also output user data to the speaker/microphone 124, the keypad 126, and/or the display/touchpad 128.
  • the processor 118 may access information from, and store data in, any type of suitable memory, such as the non-removable memory 130 and/or the removable memory 132.
  • the non-removable memory 130 may include random-access memory (RAM), read-only memory (ROM), a hard disk, or any other type of memory storage device.
  • the removable memory 132 may include a subscriber identity module (SIM) card, a memory stick, a secure digital (SD) memory card, and the like.
  • SIM subscriber identity module
  • SD secure digital
  • the processor 118 may access information from, and store data in, memory that is not physically located on the WTRU 102, such as on a server or a home computer (not shown).
  • the processor 118 may receive power from the power source 134, and may be configured to distribute and/or control the power to the other components in the WTRU 102.
  • the power source 134 may be any suitable device for powering the WTRU 102.
  • the power source 134 may include one or more dry cell batteries (e.g., nickel-cadmium (NiCd), nickel-zinc (NiZn), nickel metal hydride (NiMH), lithium-ion (Li-ion), etc.), solar cells, fuel cells, and the like.
  • the processor 118 may also be coupled to the GPS chipset 136, which may be configured to provide location information (e.g., longitude and latitude) regarding the current location of the WTRU 102.
  • the WTRU 102 may receive location information over the air interface 116 from a base station (e.g., base stations 114a, 114b) and/or determine its location based on the timing of the signals being received from two or more nearby base stations. It will be appreciated that the WTRU 102 may acquire location information by way of any suitable location-determination method while remaining consistent with an embodiment.
  • the processor 118 may further be coupled to other peripherals 138, which may include one or more software and/or hardware modules that provide additional features, functionality and/or wired or wireless connectivity.
  • the peripherals 138 may include an accelerometer, an e-compass, a satellite transceiver, a digital camera (for photographs and/or video), a universal serial bus (USB) port, a vibration device, a television transceiver, a hands free headset, a Bluetooth® module, a frequency modulated (FM) radio unit, a digital music player, a media player, a video game player module, an Internet browser, a Virtual Reality and/or Augmented Reality (VR/AR) device, an activity tracker, and the like.
  • an accelerometer an e-compass, a satellite transceiver, a digital camera (for photographs and/or video), a universal serial bus (USB) port, a vibration device, a television transceiver, a hands free headset, a Bluetooth® module, a frequency modulated (FM) radio unit, a digital music player, a media player, a video game player module, an Internet browser, a Virtual Reality and/or Augmented Reality (VR/AR) device, an activity track
  • the peripherals 138 may include one or more sensors, the sensors may be one or more of a gyroscope, an accelerometer, a hall effect sensor, a magnetometer, an orientation sensor, a proximity sensor, a temperature sensor, a time sensor; a geolocation sensor; an altimeter, a light sensor, a touch sensor, a magnetometer, a barometer, a gesture sensor, a biometric sensor, and/or a humidity sensor.
  • the WTRU 102 may include a full duplex radio for which transmission and reception of some or all of the signals (e.g., associated with particular subframes for both the UL (e.g., for transmission) and downlink (e.g., for reception) may be concurrent and/or simultaneous.
  • the full duplex radio may include an interference management unit to reduce and or substantially eliminate self-interference via either hardware (e.g., a choke) or signal processing via a processor (e.g., a separate processor (not shown) or via processor 118).
  • the WTRU 102 may include a half-duplex radio for which transmission and reception of some or all of the signals (e.g., associated with particular subframes for either the UL (e.g., for transmission) or the downlink (e.g., for reception)).
  • the WTRU is described in FIGs.1A-1B as a wireless terminal, it is contemplated that in certain representative embodiments that such a terminal may use (e.g., temporarily or permanently) wired communication interfaces with the communication network.
  • the other network 112 may be a WLAN.
  • one or more, or all, of the functions described herein may be performed by one or more emulation devices (not shown).
  • the emulation devices may be one or more devices configured to emulate one or more, or all, of the functions described herein.
  • the emulation devices may be used to test other devices and/or to simulate network and/or WTRU functions.
  • the emulation devices may be designed to implement one or more tests of other devices in a lab environment and/or in an operator network environment.
  • the one or more emulation devices may perform the one or more, or all, functions while being fully or partially implemented and/or deployed as part of a wired and/or wireless communication network in order to test other devices within the communication network.
  • the one or more emulation devices may perform the one or more, or all, functions while being temporarily implemented/deployed as part of a wired and/or wireless communication network.
  • the emulation device may be directly coupled to another device for purposes of testing and/or may performing testing using over-the-air wireless communications.
  • the one or more emulation devices may perform the one or more, including all, functions while not being implemented/deployed as part of a wired and/or wireless communication network.
  • the emulation devices may be utilized in a testing scenario in a testing laboratory and/or a non-deployed (e.g., testing) wired and/or wireless communication network in order to implement testing of one or more components.
  • the one or more emulation devices may be test equipment. Direct RF coupling and/or wireless communications via RF circuitry (e.g., which may include one or more antennas) may be used by the emulation devices to transmit and/or receive data.
  • RF circuitry e.g., which may include one or more antennas
  • FIG.1C is a system diagram illustrating an example set of interfaces for a system according to some embodiments.
  • An extended reality display device together with its control electronics, may be implemented for some embodiments.
  • System 150 can be embodied as a device including the various components described below and is configured to perform one or more of the aspects described in this document. Examples of such devices, include, but are not limited to, various electronic devices such as personal computers, laptop computers, smartphones, tablet computers, digital multimedia set top boxes, digital television receivers, personal video recording systems, connected home appliances, and servers. Elements of system 150, singly or in combination, can be embodied in a single integrated circuit (IC), multiple ICs, and/or discrete components.
  • IC integrated circuit
  • the processing and encoder/decoder elements of system 150 are distributed across multiple ICs and/or discrete components.
  • the system 150 is communicatively coupled to one or more other systems, or other electronic devices, via, for example, a communications bus or through dedicated input and/or output ports.
  • the system 150 is configured to implement one or more of the aspects described in this document.
  • the system 150 includes at least one processor 152 configured to execute instructions loaded therein for implementing, for example, the various aspects described in this document.
  • Processor 152 may include embedded memory, input output interface, and various other circuitries as known in the art.
  • the system 150 includes at least one memory 154 (e.g., a volatile memory device, and/or a non-volatile memory device).
  • System 150 may include a storage device 158, which can include non-volatile memory and/or volatile memory, including, but not limited to, Electrically Erasable Programmable Read-Only Memory (EEPROM), Read-Only Memory (ROM), Programmable Read-Only Memory (PROM), Random Access Memory (RAM), Dynamic Random Access Memory (DRAM), Static Random Access Memory (SRAM), flash, magnetic disk drive, and/or optical disk drive.
  • the storage device 158 can include an internal storage device, an attached storage device (including detachable and non-detachable storage devices), and/or a network accessible storage device, as non-limiting examples.
  • System 150 includes an encoder/decoder module 156 configured, for example, to process data to provide an encoded video or decoded video, and the encoder/decoder module 156 can include its own processor and memory.
  • the encoder/decoder module 156 represents module(s) that can be included in a device to perform the encoding and/or decoding functions. As is known, a device can include one or both of the encoding and decoding modules. Additionally, encoder/decoder module 156 can be implemented as a separate element of system 150 or can be incorporated within processor 152 as a combination of hardware and software as known to those skilled in the art.
  • Program code to be loaded onto processor 152 or encoder/decoder 156 to perform the various aspects described in this document can be stored in storage device 158 and subsequently loaded onto memory 154 for execution by processor 152.
  • one or more of processor 152, memory 154, storage device 158, and encoder/decoder module 156 can store one or more of various items during the performance of the processes described in this document. Such stored items can include, but are not limited to, the input video, the decoded video or portions of the decoded video, the bitstream, matrices, variables, and intermediate or final results from the processing of equations, formulas, operations, and operational logic.
  • memory inside of the processor 152 and/or the encoder/decoder module 156 is used to store instructions and to provide working memory for processing that is needed during encoding or decoding.
  • a memory external to the processing device (for example, the processing device can be either the processor 152 or the encoder/decoder module 152) is used for one or more of these functions.
  • the external memory can be the memory 154 and/or the storage device 158, for example, a dynamic volatile memory and/or a non-volatile flash memory.
  • an external non-volatile flash memory is used to store the operating system of, for example, a television.
  • a fast external dynamic volatile memory such as a RAM is used as working memory for video coding and decoding operations, such as for MPEG-2 (MPEG refers to the Moving Picture Experts Group, MPEG-2 is also referred to as ISO/IEC 13818, and 13818-1 is also known as H.222, and 13818-2 is also known as H.262), HEVC (HEVC refers to High Efficiency Video Coding, also known as H.265 and MPEG-H Part 2), or VVC (Versatile Video Coding, a new standard being developed by JVET, the Joint Video Experts Team).
  • MPEG-2 MPEG refers to the Moving Picture Experts Group
  • MPEG-2 is also referred to as ISO/IEC 13818
  • 13818-1 is also known as H.222
  • 13818-2 is also known as H.262
  • HEVC High Efficiency Video Coding
  • VVC Very Video Coding
  • Such input devices include, but are not limited to, (i) a radio frequency (RF) portion that receives an RF signal transmitted, for example, over the air by a broadcaster, (ii) a Component (COMP) input terminal (or a set of COMP input terminals), (iii) a Universal Serial Bus (USB) input terminal, and/or (iv) a High Definition Multimedia Interface (HDMI) input terminal.
  • RF radio frequency
  • COMP Component
  • USB Universal Serial Bus
  • HDMI High Definition Multimedia Interface
  • Other examples not shown in FIG. 1C, include composite video.
  • the input devices of block 172 have associated respective input processing elements as known in the art.
  • the RF portion can be associated with elements suitable for (i) selecting a desired frequency (also referred to as selecting a signal, or band-limiting a signal to a band of frequencies), (ii) downconverting the selected signal, (iii) band-limiting again to a narrower band of frequencies to select (for example) a signal frequency band which can be referred to as a channel in certain embodiments, (iv) demodulating the downconverted and band-limited signal, (v) performing error correction, and (vi) demultiplexing to select the desired stream of data packets.
  • a desired frequency also referred to as selecting a signal, or band-limiting a signal to a band of frequencies
  • downconverting the selected signal for example
  • band-limiting again to a narrower band of frequencies to select (for example) a signal frequency band which can be referred to as a channel in certain embodiments
  • demodulating the downconverted and band-limited signal (v) performing error correction, and (vi) demultiplexing to select the desired stream of data packets
  • the RF portion of various embodiments includes one or more elements to perform these functions, for example, frequency selectors, signal selectors, band- limiters, channel selectors, filters, downconverters, demodulators, error correctors, and demultiplexers.
  • the RF portion can include a tuner that performs various of these functions, including, for example, downconverting the received signal to a lower frequency (for example, an intermediate frequency or a near-baseband frequency) or to baseband.
  • the RF portion and its associated input processing element receives an RF signal transmitted over a wired (for example, cable) medium, and performs frequency selection by filtering, downconverting, and filtering again to a desired frequency band.
  • Adding elements can include inserting elements in between existing elements, such as, for example, inserting amplifiers and an analog-to-digital converter.
  • the RF portion includes an antenna.
  • the USB and/or HDMI terminals can include respective interface processors for connecting system 150 to other electronic devices across USB and/or HDMI connections. It is to be understood that various aspects of input processing, for example, Reed-Solomon error correction, can be implemented, for example, within a separate input processing IC or within processor 152 as necessary.
  • USB or HDMI interface processing can be implemented within separate interface ICs or within processor 152 as necessary.
  • the demodulated, error corrected, and demultiplexed stream is provided to various processing elements, including, for example, processor 152, and encoder/decoder 156 operating in combination with the memory and storage elements to process the datastream as necessary for presentation on an output device.
  • processing elements including, for example, processor 152, and encoder/decoder 156 operating in combination with the memory and storage elements to process the datastream as necessary for presentation on an output device.
  • Various elements of system 150 can be provided within an integrated housing, Within the integrated housing, the various elements can be interconnected and transmit data therebetween using suitable connection arrangement 174, for example, an internal bus as known in the art, including the Inter-IC (I2C) bus, wiring, and printed circuit boards.
  • the system 150 includes communication interface 160 that enables communication with other devices via communication channel 162.
  • the communication interface 160 can include, but is not limited to, a transceiver configured to transmit and to receive data over communication channel 162.
  • the communication interface 160 can include, but is not limited to, a modem or network card and the communication channel 162 can be implemented, for example, within a wired and/or a wireless medium.
  • Data is streamed, or otherwise provided, to the system 150, in various embodiments, using a wireless network such as a Wi-Fi network, for example IEEE 802.11 (IEEE refers to the Institute of Electrical and Electronics Engineers).
  • IEEE 802.11 IEEE refers to the Institute of Electrical and Electronics Engineers.
  • the Wi-Fi signal of these embodiments is received over the communications channel 162 and the communications interface 160 which are adapted for Wi-Fi communications.
  • the communications channel 162 of these embodiments is typically connected to an access point or router that provides access to external networks including the Internet for allowing streaming applications and other over-the-top communications.
  • Other embodiments provide streamed data to the system 150 using a set-top box that delivers the data over the HDMI connection of the input block 172.
  • Still other embodiments provide streamed data to the system 150 using the RF connection of the input block 172.
  • various embodiments provide data in a non-streaming manner.
  • various embodiments use wireless networks other than Wi-Fi, for example a cellular network or a Bluetooth network.
  • the system 150 can provide an output signal to various output devices, including a display 176, speakers 178, and other peripheral devices 180.
  • the display 176 of various embodiments includes one or more of, for example, a touchscreen display, an organic light-emitting diode (OLED) display, a curved display, and/or a foldable display.
  • the display 176 can be for a television, a tablet, a laptop, a cell phone (mobile phone), or other device.
  • the display 176 can also be integrated with other components (for example, as in a smart phone), or separate (for example, an external monitor for a laptop).
  • the other peripheral devices 180 include, in various examples of embodiments, one or more of a stand-alone digital video disc (or digital versatile disc) (DVR, for both terms), a disk player, a stereo system, and/or a lighting system.
  • DVR digital versatile disc
  • peripheral devices 180 that provide a function based on the output of the system 150.
  • a disk player performs the function of playing the output of the system 150.
  • control signals are communicated between the system 150 and the display 176, speakers 178, or other peripheral devices 180 using signaling such as AV.Link, Consumer Electronics Control (CEC), or other communications protocols that enable device-to-device control with or without user intervention.
  • the output devices can be communicatively coupled to system 150 via dedicated connections through respective interfaces 164, 166, and 168. Alternatively, the output devices can be connected to system 150 using the communications channel 162 via the communications interface 160.
  • the display 176 and speakers 178 can be integrated in a single unit with the other components of system 150 in an electronic device such as, for example, a television.
  • the display interface 164 includes a display driver, such as, for example, a timing controller (T Con) chip.
  • T Con timing controller
  • the display 176 and speaker 178 can alternatively be separate from one or more of the other components, for example, if the RF portion of input 172 is part of a separate set-top box.
  • the output signal can be provided via dedicated output connections, including, for example, HDMI ports, USB ports, or COMP outputs.
  • the system 150 may include one or more sensor devices 168.
  • sensor devices examples include one or more GPS sensors, gyroscopic sensors, accelerometers, light sensors, cameras, depth cameras, microphones, and/or magnetometers. Such sensors may be used to determine information such as user’s position and orientation.
  • the system 150 is used as the control module for an extended reality display (such as control modules 124, 132)
  • the user’s position and orientation may be used in determining how to render image data such that the user perceives the correct portion of a virtual object or virtual scene from the correct point of view.
  • the position and orientation of the device itself may be used to determine the position and orientation of the user for the purpose of rendering virtual content.
  • other inputs may be used to determine the position and orientation of the user for the purpose of rendering content.
  • a user may select and/or adjust a desired viewpoint and/or viewing direction with the use of a touch screen, keypad or keyboard, trackball, joystick, or other input.
  • the display device has sensors such as accelerometers and/or gyroscopes, the viewpoint and orientation used for the purpose of rendering content may be selected and/or adjusted based on motion of the display device.
  • the embodiments can be carried out by computer software implemented by the processor 152 or by hardware, or by a combination of hardware and software.
  • FIG.2A is a schematic side view illustrating an example waveguide display that may be used with extended reality (XR) applications according to some embodiments.
  • An image is projected by an image generator 202.
  • the image generator 202 may use one or more of various techniques for projecting an image.
  • the image generator 202 may be a laser beam scanning (LBS) projector, a liquid crystal display (LCD), a light- emitting diode (LED) display (including an organic LED (OLED) or micro LED ( ⁇ LED) display), a digital light processor (DLP), a liquid crystal on silicon (LCoS) display, or other type of image generator or light engine.
  • LBS laser beam scanning
  • LCD liquid crystal display
  • LED light- emitting diode
  • LED including an organic LED (OLED) or micro LED ( ⁇ LED) display
  • DLP digital light processor
  • LCDoS liquid crystal on silicon
  • Light representing an image 212 generated by the image generator 202 is coupled into a waveguide 204 by a diffractive in-coupler 206.
  • the in-coupler 206 diffracts the light representing the image 212 into one or more diffractive orders.
  • light ray 208 which is one of the light rays representing a portion of the bottom of the image, is diffracted by the in-coupler 206, and one of the diffracted orders 210 (e.g. the second order) is at an angle that is capable of being propagated through the waveguide 204 by total internal reflection.
  • the image generator 202 displays images as directed by a control module 224, which operates to render image data, video data, point cloud data, or other displayable data.
  • At least a portion of the light 210 that has been coupled into the waveguide 204 by the diffractive in- coupler 206 is coupled out of the waveguide by a diffractive out-coupler 214.
  • At least some of the light coupled out of the waveguide 204 replicates the incident angle of light coupled into the waveguide.
  • out-coupled light rays 216a, 216b, and 216c replicate the angle of the in-coupled light ray 208.
  • the waveguide substantially replicates the original image 212.
  • a user’s eye 218 can focus on the replicated image.
  • the out-coupler 214 out-couples only a portion of the light with each reflection allowing a single input beam (such as beam 208) to generate multiple parallel output beams (such as beams 216a, 216b, and 216c).
  • the waveguide 204 is at least partly transparent with respect to light originating outside the waveguide display.
  • the out-coupler 214 is preferably configured to let through the zero order of the real image.
  • FIG.2B is a schematic side view illustrating an example alternative display type that may be used with extended reality applications according to some embodiments.
  • a control module 232 controls a display 234, which may be an LCD, to display an image.
  • the head-mounted display includes a partly-reflective surface 236 that reflects (and in some embodiments, both reflects and focuses) the image displayed on the LCD to make the image visible to the user.
  • the partly-reflective surface 236 also allows the passage of at least some exterior light, permitting the user to see their surroundings.
  • FIG.2C is a schematic side view illustrating an example alternative display type that may be used with extended reality applications according to some embodiments.
  • a control module 242 controls a display 244, which may be an LCD, to display an image.
  • the image is focused by one or more lenses of display optics 246 to make the image visible to the user.
  • exterior light does not reach the user’s eyes directly.
  • an exterior camera 248 may be used to capture images of the exterior environment and display such images on the display 244 together with any virtual content that may also be displayed.
  • the embodiments described herein are not limited to any particular type or structure of XR display device.
  • Point Cloud (PC) data format is a universal data format across several business domains, e.g., from autonomous driving, robotics, augmented reality/virtual reality (AR/VR), civil engineering, computer graphics, to the animation/movie industry.
  • 3D LiDAR Light Detection and Ranging
  • sensors have been deployed in self- driving cars, and affordable LiDAR sensors are released from Velodyne Velabit, Apple iPad Pro 2020 and Intel RealSense LiDAR camera L515. With advances in sensing technologies, 3D point cloud data is becoming more practical than ever.
  • Point cloud data may consume a large portion of network traffic, e.g., among connected cars over 5G network, and immersive communications (VR/AR). Efficient representation formats are necessary for point cloud understanding and communication.
  • raw point cloud data may be properly organized and processed for the purposes of world modeling and sensing. Compression on raw point clouds may be used when storage and transmission of the data is required in related scenarios.
  • point clouds may represent a sequential scan of the same scene, which contains multiple moving objects. They are called dynamic point clouds as compared to static point clouds captured from a static scene or static objects. Dynamic point clouds are typically organized into frames, with different frames being captured at different times. Dynamic point clouds may require the processing and compression to be in real-time or with low delay.
  • Point Cloud Data Use Cases The automotive industry and autonomous car are domains in which point clouds may be used. Autonomous cars should be able to “probe” their environment to make good driving decisions based on the reality of their immediate surroundings. Typical sensors like LiDARs produce (dynamic) point clouds that are used by the perception engine. These point clouds are not intended to be viewed by human eyes and they are typically sparse, not necessarily colored, and dynamic with a high frequency of capture. They may have other attributes like the reflectance ratio provided by the LiDAR as this attribute is indicative of the material of the sensed object and may help in making a decision. [0130] Virtual Reality (VR) and immersive worlds are foreseen by many as the future of 2D flat video.
  • VR Virtual Reality
  • immersive worlds are foreseen by many as the future of 2D flat video.
  • Point cloud is a good format candidate to distribute VR worlds.
  • the point cloud for use in VR may be static or dynamic and are typically of average size, for example, no more than millions of points at a time.
  • Point clouds also may be used for various purposes such as culture heritage/buildings in which objects like statues or buildings are scanned in 3D in order to share the spatial configuration of the object without sending or visiting the object.
  • point clouds also may be used to ensure preservation of the knowledge of the object in case the object may be destroyed, for instance, a temple by an earthquake. Such point clouds are typically static, colored, and huge.
  • Another use case is in topography and cartography in which using 3D representations, maps are not limited to the plane and may include the relief.
  • Google Maps is a good example of 3D maps but uses meshes instead of point clouds. Nevertheless, point clouds may be a suitable data format for 3D maps and such point clouds are typically static, colored, and huge.
  • World modeling and sensing via point clouds may be a useful technology to allow machines to gain knowledge about the 3D world around them for the applications discussed herein.
  • 3D point cloud data may be discrete samples on the surfaces of objects or scenes. Such point clouds have a few characteristics. Unlike 2D images, the 3D points in a point cloud are unorganized. Secondly, depending on the how the point cloud is retrieved, the points may be distributed in a very different way, ranging from very dense (e.g., point clouds for VR/AR/gaming) to very sparse (e.g., LiDAR point clouds) distributions. Even within the same point cloud, the point distribution may vary a lot. For example, in a LiDAR point cloud, there are usually more points closer to the LiDAR sensor. Additionally, to fully represent the real world with point samples, in practice such point clouds require a huge number of points.
  • a geometry metric may be used to compare different point clouds. Given two point clouds, such as a test point cloud and a reference point cloud, the metric may output a number indicating the geometric similarity between the two input point clouds. Such a metric may be used as an objective metric to measure the performance of a point cloud processing algorithm. Secondly, given a deep neural network generating point clouds, e.g., a point cloud compression (PCC) network, the metric may be used as a loss function to compute the loss value between the reconstructed/test point cloud and the reference point cloud, so as to train the neural network.
  • PCC point cloud compression
  • a point cloud typically includes discrete samples of the surfaces of objects or scenes.
  • a geometry metric may be used to evaluate the point cloud quality, either for the evaluation of a point cloud processing/compression algorithm, or for the loss value computation in deep neural network training.
  • creating such a metric may be challenging due to the specialty of the point cloud format. This work specifically focuses on this problem of designing a similarity metric for point clouds.
  • a geometry metric may be used to evaluate the quality of a test point cloud given a reference point cloud.
  • Chamfer Distance is a popular metric for comparing point clouds. See Fan, Haoqiang, et al., A Point Set Generation Network for 3D Object Reconstruction from a Single Image, PROCEEDINGS OF THE IEEE CONF. ON COMP. VISION AND PATTERN RECOGNITION (2017) (“Fan”). Suppose the 3D point clouds being compared are ⁇ and ⁇ in which ⁇ contains n points and Y contains m points.
  • the Chamfer distance between ⁇ and ⁇ is shown in Eq.1: ⁇ ⁇ , ⁇ ⁇ ⁇ ⁇ ⁇ m ⁇ i ⁇ n ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ m ⁇ i ⁇ n ⁇ ⁇ ⁇ ⁇ ⁇ (1) in which ⁇ and ⁇ are 3D points in ⁇ and ⁇ , respectively. For each point in ⁇ and ⁇ , the CD algorithm looks for the nearest neighbor from the other point cloud, calculates the distance, and sums the distances. [0140] Using CD as a loss function in deep neural network training may be problematic.
  • a neural network trained with the Chamfer Distance often outputs point clouds with a very unbalanced density, e.g., certain regions may have much higher density than others.
  • Achlioptas, Panos, et al. Learning Representations and Generative Models for 3D Point Clouds, INTERN’L CONF. ON MACH. LEARNING, PMLR (2016).
  • Earth Mover’s Distance (EMD) [0141]
  • EMD Earth Mover’s Distance
  • Another popular metric is the Earth Mover’s Distance (EMD). See Fan and Rubner, Yossi, et al., The Earth Mover's Distance as a Metric for Image Retrieval, 40 INTERN’L J. COMP. VISION 99-121 (2000).
  • EMD metric the shortest aggregated distance.
  • the EMD distance is understood to have two limitations: the number of points in ⁇ and the number of points in ⁇ must both be the same.
  • the computational cost of computing the EMD distance is very high.
  • Point-to-Plane Distance measures point-to-point distance between two comparing point clouds
  • the point-to-plane distance measures average point-to-plane distance by utilizing the surface normal of the reference point cloud.
  • point cloud ⁇ is the reference point cloud (which may be called the ground-truth point cloud)
  • point cloud ⁇ is the test point cloud (which may be called the reconstructed point cloud).
  • the point-to-plane distance may be expressed as shown in Eq.3: ⁇ ⁇ , ⁇ ⁇ ⁇ ⁇ ⁇ m ⁇ ⁇ ⁇ ⁇ ⁇ (3) in which ⁇ ⁇ is the normal point on the ground-truth ⁇ and looks for a point in ⁇ that has a minimum projected distance according to the surface normal.
  • the surface normal of ⁇ may not be available and may need to be estimated.
  • the normal vector may not even be well defined. Particularly, there is no normal vector for line or curve structures.
  • a geometry metric may be used to evaluate the quality of a test point cloud ⁇ given a reference point cloud ⁇ . This metric will be called the Local Mahalanobis Distance (LMD).
  • LMD Local Mahalanobis Distance
  • FIG.3 is a schematic illustration showing an example estimation of a covariance matrix for a reference point cloud according to some embodiments. Given a refence point cloud ⁇ (302) with ⁇ points and a test point cloud ⁇ with ⁇ points, the LMD distance may be used to evaluate the quality of the test point cloud ⁇ .
  • a 3-by-3 covariance matrix ⁇ (308) for ⁇ ⁇ 0, 1, 2, ... , ⁇ ⁇ 1 ⁇ may be calculated to describe the local geometry, where again, ⁇ is the number of points in ⁇ .
  • An example process 300 is shown in FIG.3.
  • a nearest neighbor search 304 of the ⁇ nearest neighbors of point ⁇ ⁇ ⁇ ⁇ ⁇ are formed into a point set ⁇ ⁇ ⁇ , ⁇ , ... , ⁇ ⁇ .
  • the estimated covariance matrix ⁇ ⁇ associated with ⁇ ⁇ is given by Eq.4: ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ (4) [0147] In som the points in ⁇ ⁇ . [0148] For each point ⁇ ⁇ in the reference point cloud ⁇ , a search is performed for the ⁇ nearest neighbors within the test point cloud ⁇ . The set of neighboring points in ⁇ associated with point ⁇ ⁇ is denoted as ⁇ ⁇ .
  • the LMD between ⁇ and ⁇ may be determined as shown in Eq.5: ⁇ ⁇ , ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ , ⁇ ; ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ , ⁇ ; ⁇ (5) in which ⁇ ⁇ and ⁇ .
  • the Mahalanobis distance between ⁇ ⁇ and ⁇ may be expressed as shown in Eq.6: ⁇ ⁇ ⁇ , ⁇ ; ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ (6) in which “T” means the transpose operation, and ⁇ ⁇ ⁇ is the inverse of the covariance matrix ⁇ ⁇ .
  • the computation of the LMD may be split into two steps, as also shown by the first equal sign of Eq.5.
  • the Mahalanobis distance between ⁇ ⁇ and all the ⁇ points from ⁇ in the neighborhood ⁇ ⁇ is determined, followed by averaging the ⁇ distance values, which may be part of the PCA process 306 shown in FIG.3.
  • the averaged Mahalanobis distances ⁇ ⁇ ⁇ ⁇ , ⁇ ; ⁇ for all the ⁇ ’s from ⁇ are further averaged, leading to the LMD between ⁇ and ⁇ .
  • the distances is used instead of averaging the distance values for aggregation in the above two steps.
  • FIG.4 is a metric according to some measures a ⁇ ⁇ and a point ⁇ considering the local geometry of the reference point cloud ⁇ 402 at position ⁇ ⁇ .
  • FIG.4 shows an environment 400 with isolines 404, 406 of the Mahalanobis distance at points ⁇ and ⁇ .
  • the isolines 404, 406 of Mahalanobis distances are ellipses oriented along the surface orientations at ⁇ and ⁇ , respectively.
  • a point having the same Euclidean distance to ⁇ or ⁇ may have very different Mahalanobis distance, depending on whether this point is close to the surface.
  • both ⁇ and ⁇ have the same Euclidean distance to ⁇ .
  • the point ⁇ would lead to a much smaller Mahalanobis distance to ⁇ .
  • point ⁇ has a much larger Mahalanobis distance to ⁇ because the line segment ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ crosses 4 isolines 404.
  • FIG.5 is a flowchart illustrating an example process for determining an LMD metric according to some embodiments. The steps to reflect the computation of LMD with Eq.5 are shown in the example process 500 of FIG.5. The covariance matrix ⁇ ⁇ is constructed 502, and the nearest neighbor set ⁇ ⁇ for each point ⁇ ⁇ in ⁇ is constructed 504.
  • a set of Mahalanobis distances ⁇ ⁇ between ⁇ ⁇ and the points in ⁇ ⁇ is computed 506 using ⁇ ⁇ , which leads to a distance set ⁇ ⁇ for each point ⁇ ⁇ in the reference point cloud ⁇ .
  • the distance from the point to ⁇ is computed 508 by averaging the distances in the distance set ⁇ ⁇ .
  • the distance values associated with the points ⁇ ⁇ in ⁇ are averaged 510, which leads to the LMD value ⁇ ⁇ ⁇ , ⁇ between ⁇ and ⁇ .
  • LMD may be written as shown in Eq.8: ⁇ ⁇ , ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ , ⁇ ; ⁇ (8) Compared to Eq.5, Eq.8 [0152] In some shown in Eq.9: ⁇ ⁇ ′ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ (9) in which ⁇ is a 3-by-3 identity matrix and ⁇ is a small positive constant. Adding the term ⁇ ⁇ ⁇ to the covariance matrix makes the covariance matrix more stable during the matrix inverse, where ⁇ is a small positive real number, e.g., 0.001.
  • the LMD may be applied as a loss function to train a deep neural network for point cloud generation.
  • the covariance matrices ⁇ ⁇ associated with reference point clouds may be precomputed before training. In this way, there is no need to compute the covariance matrices on the fly during training, which may reduce the computational cost.
  • Enhanced LMD [0154] Eq.5 iterates every point in the reference point cloud ⁇ . However, the computation of Eq.5 may not cover all points in the test point cloud ⁇ , especially for those points in ⁇ that are far away from ⁇ . Thus, some points in ⁇ may not be considered in the evaluation.
  • a second term may be computed from the perspective of the test point cloud ⁇ may be included in the An updated LMD is shown in Eq.10: ⁇ ⁇ ⁇ , ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ , ⁇ ; ⁇ ⁇ ⁇ ⁇ ⁇ (10) [0156] point in the point cloud ⁇ .
  • ⁇ is the covariance matrix associated with 3D point ⁇ .
  • the numbers ⁇ and ⁇ may be different numbers, e.g., ⁇ ⁇ 7 and ⁇ ⁇ 1.
  • FIG.6 is a flowchart illustrating an example process for dynamic training of a deep neural network with an LMD according to some embodiments. In FIG.6, the LMD is changed dynamically for better geometry detail reconstructions.
  • the LMD may be applied as a loss function for deep neural network training.
  • the parameter K for the nearest neighbor search plays an important role. If K is a bigger number, the covariance matrices characterize the geometry of a larger region of the reference point cloud. In such a first scenario, the LMD may be more suitable to train a deep neural network for reconstructing the rough point cloud shapes. Stated differently, if K is a smaller number, the covariance matrices characterize only a very local region. In such a second scenario, the LMD may be more suitable to train a deep neural network for reconstructing finer details.
  • the LMD loss may be dynamically changed during neural network training, which gradually decreases the number K as the training goes on.
  • the network may be trained 602 with LMD ⁇ for ⁇ ⁇ epochs, in which ⁇ ⁇ is relatively larger. At the beginning of training, the network may learn how to reconstruct the point clouds roughly.
  • the network may be trained 604 with LMD ⁇ for ⁇ epochs in which ⁇ ⁇ ⁇ .
  • the neural network may be trained 606 with LMD ⁇ for ⁇ epochs in which ⁇ ⁇ ⁇ .
  • the neural network may be trained 608 with LMD ⁇ for ⁇ ⁇ epochs.
  • the deep neural network may gradually learn how to reconstruct the point cloud geometry details.
  • FIG.7 is a flowchart illustrating an example process for determining a Local Mahalanobis Distance (LMD) metric according to some embodiments.
  • an example process 700 may include performing a process loop for each point in a reference point cloud.
  • the example process 700 may further include selecting 702 a first point equal to the respective point in the reference point cloud for a current pass through the process loop. For some embodiments, the example process 700 may further include obtaining 704 a local covariance. For some embodiments, the example process 700 may further include determining 706 a distance set, wherein the distance set includes Mahalanobis distances between the first point and each of the plurality of nearest neighboring points in a test point cloud. For some embodiments, the example process 700 may determine 708 if the process loop has more passes to do. If the process loop has more passes to do, control returns to do the next pass; otherwise, the process loop is exited.
  • the example process 700 may further include determining 710 a first distance by aggregating the distance set associated with each point in the reference point cloud.
  • the example process 700 may further include determining 712 a second distance between the test point cloud and the reference point cloud, by aggregating each of the first distances associated with each respective point of the reference point cloud.
  • XR extended reality
  • some embodiments may be applied to any XR contexts such as, e.g., virtual reality (VR) / mixed reality (MR) / augmented reality (AR) contexts.
  • VR virtual reality
  • MR mixed reality
  • AR augmented reality
  • head mounted display HMD
  • some embodiments may be applied to a wearable device (which may or may not be attached to the head) capable of, e.g., XR, VR, AR, and/or MR for some embodiments.
  • a first example method in accordance with some embodiments may include: accessing, for each point in a reference point cloud, a local covariance; accessing, for each point in the reference point cloud, a plurality of nearest neighboring points in a test point cloud; determining, for each point in the reference point cloud, a distance set, wherein the distance set includes Mahalanobis distances between a first point in the reference point cloud and a second point of the plurality of nearest neighboring points in the test point cloud; determining, for each point in the reference point cloud, a first distance by aggregating the distance set associated with each point in the reference point cloud; and determining a second distance between the test point cloud and the reference point cloud, by aggregating each of the first distances associated with each respective point of the reference point cloud.
  • aggregating the distance set associated with each point in the reference point cloud comprises averaging the distance set associated with each point in the reference cloud.
  • aggregating each of the first distances associated with each respective point of the reference point cloud comprises determining a maximum of the first distances associated with each respective point of the reference point cloud.
  • aggregating each of the first distances associated with each respective point of the reference point cloud comprises averaging each of the first distances associated with each respective point in the reference point cloud.
  • a second example method in accordance with some embodiments may include: performing, for each point in a reference point cloud: obtaining a local covariance for the point in the reference point cloud; determining a distance set for the point in the reference point cloud, wherein the distance set includes Mahalanobis distances between the point in the reference point cloud and each of a plurality of nearest neighboring points in a test point cloud; and determining a first distance corresponding to the point in the reference point cloud by aggregating the distance set associated with the point in the reference point cloud; and determining a second distance between the test point cloud and the reference point cloud, by aggregating each of the first distances associated with each respective point of the reference point cloud.
  • aggregating the distance set associated with the point in the reference point cloud comprises determining a maximum of the distance set associated with the point in the reference point cloud. [0170] For some embodiments of the second example method, aggregating the distance set associated with the point in the reference point cloud comprises averaging the distance set associated with the point in the reference point cloud. [0171] For some embodiments of the second example method, averaging the distance set associated with the point in the reference point cloud comprises averaging the Mahalanobis distances with the distance set associated with the point in the reference point cloud.
  • aggregating each of the first distances associated with each respective point of the reference point cloud comprises determining a maximum of the first distances associated with each respective point of the reference point cloud. [0173] For some embodiments of the second example method, aggregating each of the first distances associated with each respective point of the reference point cloud comprises averaging each of the first distances associated with each respective point in the reference point cloud. [0174] For some embodiments of the second example method, obtaining the local covariance includes determining the local covariance.
  • the local covariance for the point in the reference point cloud includes a covariance matrix comprising distances between the point in the reference point cloud and each of a plurality of nearest neighboring points in the reference point cloud.
  • a third example method in accordance with some embodiments may include: performing a process loop for each point in a reference point cloud, wherein the process loop includes: selecting a first point equal to the respective point in the reference point cloud for a current pass through the process loop; obtaining a local covariance; determining a distance set, wherein the distance set includes Mahalanobis distances between the first point and each of the plurality of nearest neighboring points in a test point cloud; and determining a first distance by aggregating the distance set associated with each point in the reference point cloud; and determining a second distance between the test point cloud and the reference point cloud, by aggregating each of the first distances associated with each respective point of the reference point cloud.
  • aggregating the distance set associated with each point in the reference point cloud comprises determining a maximum of the distance set associated with each point in the reference point cloud. [0178] For some embodiments of the third example method, aggregating the distance set associated with each point in the reference point cloud comprises averaging the distance set associated with each point in the reference point cloud. [0179] For some embodiments of the third example method, aggregating each of the first distances associated with each respective point of the reference point cloud comprises averaging each of the first distances associated with each respective point in the reference point cloud.
  • performing the process loop for each point in the reference point cloud is performed at least partially in parallel for one or more points in the reference point cloud at the same time.
  • performing the process loop for each point in the reference point cloud is performed serially.
  • obtaining the local covariance includes determining the local covariance.
  • the local covariance for the point in the reference point cloud includes a covariance matrix comprising distances between the point in the reference point cloud and each of a plurality of nearest neighboring points in the reference point cloud.
  • determining the local covariance includes using a covariance matrix corresponding to the plurality of nearest neighbors of the first point. [0185] 1 For some embodiments of the third example method, determining the covariance matrix includes using an average of the plurality of nearest neighbors of the first point. [0186] For some embodiments of the third example method, determining the covariance matrix includes determining a series of differences between the first point and each of the plurality of nearest neighbors of the first point. [0187] For some embodiments of the third example method, determining the local covariance includes adding a small offset to one or more elements of the covariance matrix.
  • Some embodiments of the third example method may further include using the second distance as a loss function to train a deep neural network, wherein the covariance matrix is determined before training the deep neural network. [0189] Some embodiments of the third example method may further include changing the second distance dynamically during training of the deep neural network. [0190] For some embodiments of the third example method, changing the second distance dynamically includes adjusting how many nearest neighboring points are in the test point cloud. [0191] For some embodiments of the third example method, changing the second distance dynamically includes decreasing how many nearest neighboring points are in the test point cloud.
  • determining the local covariance includes using an inverse of a covariance matrix corresponding to the plurality of nearest neighbors of the first point.
  • Some embodiments of the third example method may further include: determining a second local covariance based on the reference point cloud; determining a third distance based on the second local covariance; and adding the third distance to the second distance to obtain an enhanced distance.
  • a third example apparatus in accordance with some embodiments may include: a processor; and a non-transitory computer-readable medium storing instructions operative, when executed by the processor, to cause the apparatus to: perform a process loop for each point in a reference point cloud, wherein the process loop includes: selecting a first point equal to the respective point in the reference point cloud for a current pass through the process loop; obtaining a local covariance; determining a distance set, wherein the distance set includes Mahalanobis distances between the first point and each of the plurality of nearest neighboring points in a test point cloud; and determine a first distance by aggregating the distance set associated with each point in the reference point cloud; and determine a second distance between the test point cloud and the reference point cloud, by aggregating each of the first distances associated with each respective point of the reference point cloud.
  • a fourth example method in accordance with some embodiments may include: performing a process loop for each point in a reference point cloud, wherein the process loop includes: selecting a first point equal to the respective point in the reference point cloud for a current pass through the process loop; obtaining a local covariance; determining a distance set, wherein the distance set includes square roots of Mahalanobis distances between the first point and each of the plurality of nearest neighboring points in a test point cloud; and determining a first distance by aggregating the distance set associated with each point in the reference point cloud; and determining a second distance between the test point cloud and the reference point cloud, by aggregating each of the first distances associated with each respective point of the reference point cloud.
  • aggregating the distance set associated with each point in the reference point cloud includes determining a maximum of the distance set associated with each point in the reference point cloud. [0197] For some embodiments of the fourth example method, aggregating the distance set associated with each point in the reference point cloud includes averaging the distance set associated with each point in the reference point cloud. [0198] For some embodiments of the fourth example method, averaging the distance set associated with each point in the reference point cloud includes averaging over the plurality of nearest neighboring points in the test point cloud. [0199] For some embodiments of the fourth example method, determining the local covariance includes using a covariance matrix corresponding to the plurality of nearest neighbors of the first point.
  • determining the covariance matrix includes using an average of the plurality of nearest neighbors of the first point. [0201] For some embodiments of the fourth example method, determining the covariance matrix includes determining a series of differences between the first point and each of the plurality of nearest neighbors of the first point. [0202] For some embodiments of the fourth example method, determining the local covariance includes adding a small offset to one or more elements of the covariance matrix. [0203] Some embodiments of the fourth example method may further include using the second distance as a loss function to train a deep neural network, wherein the covariance matrix is determined before training the deep neural network.
  • Some embodiments of the fourth example method may further include changing the second distance dynamically during training of the deep neural network.
  • changing the second distance dynamically includes adjusting how many nearest neighboring points are in the test point cloud.
  • changing the second distance dynamically includes decreasing how many nearest neighboring points are in the test point cloud.
  • determining the local covariance includes using an inverse of a covariance matrix corresponding to the plurality of nearest neighbors of the first point.
  • Some embodiments of the fourth example method may further include: determining a second local covariance based on the reference point cloud; determining a third distance based on the second local covariance; and adding the third distance to the second distance to obtain an enhanced distance.
  • a fourth example apparatus in accordance with some embodiments may include: a processor; and a non-transitory computer-readable medium storing instructions operative, when executed by the processor, to cause the apparatus to: perform a process loop for each point in a reference point cloud, wherein the process loop includes: selecting a first point equal to the respective point in the reference point cloud for a current pass through the process loop; obtaining a local covariance; determining a distance set, wherein the distance set includes square roots of Mahalanobis distances between the first point and each of the plurality of nearest neighboring points in a test point cloud; and determine a first distance by aggregating the distance set associated with each point in the reference point cloud; and determine a second distance between the test point cloud and the reference point cloud, by aggregating each of the first distances associated with each respective point of the reference point cloud.
  • a fifth example apparatus in accordance with some embodiments may include at least one processor configured to perform any one of the above methods.
  • a sixth example apparatus in accordance with some embodiments may include a computer-readable medium storing instructions for causing one or more processors to perform any one of the above methods.
  • a seventh example apparatus in accordance with some embodiments may include at least one processor and at least one non-transitory computer-readable medium storing instructions for causing the at least one processor to perform any one of the above methods.
  • An example signal in accordance with some embodiments may include a bitstream generated according to any one of the above methods. [0214] This disclosure describes a variety of aspects, including tools, features, embodiments, models, approaches, etc.
  • At least one of the aspects generally relates to video encoding and decoding, and at least one other aspect generally relates to transmitting a bitstream generated or encoded.
  • HDR high dynamic range
  • SDR standard dynamic range
  • HDR high dynamic range
  • SDR standard dynamic range
  • a reference to HDR is understood to mean “higher dynamic range”
  • a reference to SDR is understood to mean “lower dynamic range.”
  • Such additional embodiments are not constrained by any specific values of dynamic range that might often be associated with the terms “high dynamic range” and “standard dynamic range.”
  • Various methods are described herein, and each of the methods comprises one or more steps or actions for achieving the described method. Unless a specific order of steps or actions is required for proper operation of the method, the order and/or use of specific steps and/or actions may be modified or combined.
  • first”, “second”, etc. may be used in various embodiments to modify an element, component, step, operation, etc., such as, for example, a “first decoding” and a “second decoding”. Use of such terms does not imply an ordering to the modified operations unless specifically required. So, in this example, the first decoding need not be performed before the second decoding, and may occur, for example, before, during, or in an overlapping time period with the second decoding.
  • Various numeric values may be used in the present disclosure, for example. The specific values are for example purposes and the aspects described are not limited to these specific values.
  • Embodiments described herein may be carried out by computer software implemented by a processor or other hardware, or by a combination of hardware and software.
  • the embodiments can be implemented by one or more integrated circuits.
  • the processor can be of any type appropriate to the technical environment and can encompass one or more of microprocessors, general purpose computers, special purpose computers, and processors based on a multi-core architecture, as non-limiting examples.
  • Various implementations involve decoding. “Decoding”, as used in this disclosure, can encompass all or part of the processes performed, for example, on a received encoded sequence in order to produce a final output suitable for display.
  • such processes include one or more of the processes typically performed by a decoder, for example, entropy decoding, inverse quantization, inverse transformation, and differential decoding.
  • processes also, or alternatively, include processes performed by a decoder of various implementations described in this disclosure, for example, extracting a picture from a tiled (packed) picture, determining an upsampling filter to use and then upsampling a picture, and flipping a picture back to its intended orientation.
  • decoding refers only to entropy decoding
  • decoding refers only to differential decoding
  • decoding refers to a combination of entropy decoding and differential decoding. Whether the phrase “decoding process” is intended to refer specifically to a subset of operations or generally to the broader decoding process will be clear based on the context of the specific descriptions.
  • Various implementations involve encoding. In an analogous way to the above discussion about “decoding”, “encoding” as used in this disclosure can encompass all or part of the processes performed, for example, on an input video sequence in order to produce an encoded bitstream.
  • such processes include one or more of the processes typically performed by an encoder, for example, partitioning, differential encoding, transformation, quantization, and entropy encoding. In various embodiments, such processes also, or alternatively, include processes performed by an encoder of various implementations described in this disclosure.
  • encoding refers only to entropy encoding
  • encoding refers only to differential encoding
  • encoding refers to a combination of differential encoding and entropy encoding. Whether the phrase “encoding process” is intended to refer specifically to a subset of operations or generally to the broader encoding process will be clear based on the context of the specific descriptions.
  • a figure When a figure is presented as a flow diagram, it should be understood that it also provides a block diagram of a corresponding apparatus. Similarly, when a figure is presented as a block diagram, it should be understood that it also provides a flow diagram of a corresponding method/process.
  • Various embodiments refer to rate distortion optimization.
  • the balance or trade-off between the rate and distortion is usually considered, often given the constraints of computational complexity.
  • the rate distortion optimization is usually formulated as minimizing a rate distortion function, which is a weighted sum of the rate and of the distortion. There are different approaches to solve the rate distortion optimization problem.
  • the approaches may be based on an extensive testing of all encoding options, including all considered modes or coding parameters values, with a complete evaluation of their coding cost and related distortion of the reconstructed signal after coding and decoding.
  • Faster approaches may also be used, to save encoding complexity, in particular with computation of an approximated distortion based on the prediction or the prediction residual signal, not the reconstructed one.
  • a mix of these two approaches can also be used, such as by using an approximated distortion for only some of the possible encoding options, and a complete distortion for other encoding options.
  • Other approaches only evaluate a subset of the possible encoding options.
  • a processor which refers to processing devices in general, including, for example, a computer, a microprocessor, an integrated circuit, or a programmable logic device.
  • Processors also include communication devices, such as, for example, computers, cell phones, portable/personal digital assistants (“PDAs”), and other devices that facilitate communication of information between end-users.
  • PDAs portable/personal digital assistants
  • this disclosure may refer to “determining” various pieces of information. Determining the information can include one or more of, for example, estimating the information, calculating the information, predicting the information, or retrieving the information from memory. [0230] Further, this disclosure may refer to “accessing” various pieces of information.
  • Accessing the information can include one or more of, for example, receiving the information, retrieving the information (for example, from memory), storing the information, moving the information, copying the information, calculating the information, determining the information, predicting the information, or estimating the information. [0231] Additionally, this disclosure may refer to “receiving” various pieces of information. Receiving is, as with “accessing”, intended to be a broad term. Receiving the information can include one or more of, for example, accessing the information, or retrieving the information (for example, from memory).
  • “receiving” is typically involved, in one way or another, during operations such as, for example, storing the information, processing the information, transmitting the information, moving the information, copying the information, erasing the information, calculating the information, determining the information, predicting the information, or estimating the information.
  • “/”, “and/or”, and “at least one of”, for example, in the cases of “A/B”, “A and/or B” and “at least one of A and B” is intended to encompass the selection of the first listed option (A) only, or the selection of the second listed option (B) only, or the selection of both options (A and B).
  • such phrasing is intended to encompass the selection of the first listed option (A) only, or the selection of the second listed option (B) only, or the selection of the third listed option (C) only, or the selection of the first and the second listed options (A and B) only, or the selection of the first and third listed options (A and C) only, or the selection of the second and third listed options (B and C) only, or the selection of all three options (A and B and C).
  • This may be extended for as many items as are listed.
  • the word “signal” refers to, among other things, indicating something to a corresponding decoder.
  • the encoder signals a particular one of a plurality of parameters for region-based filter parameter selection for de-artifact filtering.
  • the same parameter is used at both the encoder side and the decoder side.
  • an encoder can transmit (explicit signaling) a particular parameter to the decoder so that the decoder can use the same particular parameter.
  • signaling can be used without transmitting (implicit signaling) to simply allow the decoder to know and select the particular parameter.
  • signaling can be accomplished in a variety of ways. For example, one or more syntax elements, flags, and so forth are used to signal information to a corresponding decoder in various embodiments. While the preceding relates to the verb form of the word “signal”, the word “signal” can also be used herein as a noun.
  • Implementations can produce a variety of signals formatted to carry information that can be, for example, stored or transmitted. The information can include, for example, instructions for performing a method, or data produced by one of the described implementations. For example, a signal can be formatted to carry the bitstream of a described embodiment.
  • Such a signal can be formatted, for example, as an electromagnetic wave (for example, using a radio frequency portion of spectrum) or as a baseband signal.
  • the formatting can include, for example, encoding a data stream and modulating a carrier with the encoded data stream.
  • the information that the signal carries can be, for example, analog or digital information.
  • the signal can be transmitted over a variety of different wired or wireless links, as is known.
  • the signal can be stored on a processor-readable medium.
  • embodiments can include one or more of the following features, devices, or aspects, alone or in any combination, across various claim categories and types: ⁇ Adapting residues at an encoder according to any of the embodiments discussed. ⁇ A bitstream or signal that includes one or more of the described syntax elements, or variations thereof. ⁇ A bitstream or signal that includes syntax conveying information generated according to any of the embodiments described. ⁇ Inserting in the signaling syntax elements that enable the decoder to adapt residues in a manner corresponding to that used by an encoder. ⁇ Creating and/or transmitting and/or receiving and/or decoding a bitstream or signal that includes one or more of the described syntax elements, or variations thereof.
  • modules that carry out (i.e., perform, execute, and the like) various functions that are described herein in connection with the respective modules.
  • a module includes hardware (e.g., one or more processors, one or more microprocessors, one or more microcontrollers, one or more microchips, one or more application-specific integrated circuits (ASICs), one or more field programmable gate arrays (FPGAs), one or more memory devices) deemed suitable by those of skill in the relevant art for a given implementation.
  • hardware e.g., one or more processors, one or more microprocessors, one or more microcontrollers, one or more microchips, one or more application-specific integrated circuits (ASICs), one or more field programmable gate arrays (FPGAs), one or more memory devices
  • Each described module may also include instructions executable for carrying out the one or more functions described as being carried out by the respective module, and it is noted that those instructions could take the form of or include hardware (i.e., hardwired) instructions, firmware instructions, software instructions, and/or the like, and may be stored in any suitable non-transitory computer-readable medium or media, such as commonly referred to as RAM, ROM, etc.
  • RAM random access memory
  • ROM read-only memory
  • Examples of computer-readable storage media include, but are not limited to, a read only memory (ROM), a random access memory (RAM), a register, cache memory, semiconductor memory devices, magnetic media such as internal hard disks and removable disks, magneto-optical media, and optical media such as CD-ROM disks, and digital versatile disks (DVDs).
  • ROM read only memory
  • RAM random access memory
  • register cache memory
  • semiconductor memory devices magnetic media such as internal hard disks and removable disks, magneto-optical media, and optical media such as CD-ROM disks, and digital versatile disks (DVDs).
  • a processor in association with software may be used to implement a radio frequency transceiver for use in a WTRU, UE, terminal, base station, RNC, or any host computer.

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Abstract

Certains modes de réalisation d'un procédé peuvent consister à : réaliser une boucle de traitement pour chaque point dans un nuage de points de référence, la boucle de traitement consistant à : sélectionner un premier point égal au point respectif dans le nuage de points de référence aux fins d'un passage de courant à travers la boucle de traitement ; obtenir une covariance locale ; déterminer un ensemble de distances, l'ensemble de distances comprenant des distances de Mahalanobis entre le premier point et chaque point de la pluralité de points voisins les plus proches dans un nuage de points de test ; et déterminer une première distance par agrégation de l'ensemble de distances associé à chaque point dans le nuage de points de référence ; et déterminer une seconde distance entre le nuage de points de test et le nuage de points de référence, par agrégation de chaque distance des premières distances associées à chaque point respectif du nuage de points de référence.
PCT/US2024/054981 2023-11-09 2024-11-07 Mesure de géométrie adaptative pour des nuages de points 3d Pending WO2025101793A1 (fr)

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