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WO2025011956A1 - Correction de mouvement pour prédiction d'attributs de nuage de points - Google Patents

Correction de mouvement pour prédiction d'attributs de nuage de points Download PDF

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Publication number
WO2025011956A1
WO2025011956A1 PCT/EP2024/067887 EP2024067887W WO2025011956A1 WO 2025011956 A1 WO2025011956 A1 WO 2025011956A1 EP 2024067887 W EP2024067887 W EP 2024067887W WO 2025011956 A1 WO2025011956 A1 WO 2025011956A1
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WIPO (PCT)
Prior art keywords
point cloud
cloud frame
current point
geometry
attribute
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
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PCT/EP2024/067887
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English (en)
Inventor
Bertrand Chupeau
Gustavo Sandri
Franck Thudor
Maja KRIVOKUCA
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InterDigital CE Patent Holdings SAS
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InterDigital CE Patent Holdings SAS
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Publication of WO2025011956A1 publication Critical patent/WO2025011956A1/fr
Pending legal-status Critical Current
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Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T9/00—Image coding
    • G06T9/001—Model-based coding, e.g. wire frame
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T9/00—Image coding
    • G06T9/004—Predictors, e.g. intraframe, interframe coding
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/50—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/50—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding
    • H04N19/597—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding specially adapted for multi-view video sequence encoding

Definitions

  • Point clouds have arisen as one of the main 3D scene representations for such applications.
  • a point cloud frame consists of a set of 3D points, each point being represented with its 3D position and possibly several attributes such as colour, transparency, reflectance, etc.
  • Standardization activity for point cloud compression is being carried out by the ISO/IEC JTC1/SC29/WG7 “MPEG 3D Graphics and Haptics Coding” group, as described in D. Graziosi et al., “An overview of ongoing point cloud compression standardization activities: video-based (V-PCC) and geometry-based (G-PCC),” APSIPA Transactions on Signal and Information Processing, Volume 9, Vol. 9, No. 1 , e13, 2020.
  • Various 3D motion estimation algorithms are available for use in a point cloud compression scheme with motion compensated inter-frame prediction, such as those based on an iterative closest point algorithm (ICP), block matching, or graph matching.
  • ICP iterative closest point algorithm
  • block matching block matching
  • graph matching graph matching
  • a point cloud decoding method comprises obtaining geometry-based motion information M geo for a current point cloud frame; obtaining attributebased motion information 6M att for the current point cloud frame; and predicting attributes of the current point cloud frame based on a composition 8M att ° M geo of the geometry-based motion information and the attribute-based motion information applied to a reference point cloud frame
  • predicting attributes of the current point cloud frame comprises: generating a motion compensated point cloud frame ⁇ Gf, f ⁇ by applying, to the reference point cloud frame the composition 6M att ° M geo of the geometry-based motion information and the attribute-based motion information; generating a reconstructed geometry G t of the current point cloud frame; and predicting attributes of the current point cloud frame based on the reconstructed geometry G t of the current point cloud frame and the motion compensated point cloud frame ⁇ Gf, f ⁇ -
  • Some embodiments further include: generating an inter prediction Gf of a geometry of the current point cloud frame by applying the geometry-based motion information M geo to a geometry G t-1 of the reference point cloud frame; and determining a reconstructed geometry G t of the current point cloud frame based on the inter prediction Gf .
  • Some embodiments further include generating an inter prediction Gf of a geometry of the current point cloud frame by applying the geometry-based motion information M geo to a geometry G t-1 of the reference point cloud frame; wherein the reconstructed geometry G t of the current point cloud frame is based on the inter prediction Gf .
  • the attribute-based motion information 6M att consists of translation information.
  • the attribute-based motion information 6M att comprises translation information and rotation information.
  • a point cloud decoding method comprises: obtaining geometry-based motion information M geo for a current point cloud frame; generating a first motion compensated prediction ⁇ G aeo ,A p t aeo ] of the current point cloud frame based on the geometry-based motion information applied to a reference point cloud frame obtaining attribute-based motion information 6M att for the current point cloud frame; and predicting of attributes of the current point cloud frame based on the attributebased motion information 6M att applied to the first motion compensated prediction ⁇ G P fleo A p aeo
  • predicting attributes of the current point cloud frame comprises: generating a second motion compensated prediction ⁇ G P ,A P ⁇ of the current point cloud frame by applying, to the first motion compensated prediction ⁇ G p aeo ,A p aeo ⁇ , the attribute-based motion information 6M att determining a reconstructed geometry G t of the current point cloud frame; and predicting attributes of the current point cloud frame based on the reconstructed geometry G t of the current point cloud frame and the second motion compensated prediction [Gf,A 3 ].
  • Some embodiments further include: generating a reconstructed geometry G t of the current point cloud frame based on a geometry G 3 aeo of the first motion compensated prediction of the current point cloud frame.
  • the attribute-based motion information 6M att consists of translation information.
  • the attribute-based motion information 6M att comprises translation information and rotation information.
  • a point cloud encoding method comprises: determining geometry-based motion information M geo for a current point cloud frame; determining attribute-based motion information 6M att for the current point cloud frame; and predicting attributes of the current point cloud frame based on a composition 6M att ° M geo of the geometry-based motion information and the attribute-based motion information applied to a reference point cloud frame
  • predicting attributes of the current point cloud frame comprises: generating a motion compensated point cloud frame ⁇ G 3 ,A 3 ⁇ by applying, to the reference point cloud frame A t-t ⁇ , the composition 6M att ° M geo of the geometry-based motion information and the attribute-based motion information; generating a reconstructed geometry G t of the current point cloud frame; and predicting attributes of the current point cloud frame based on the reconstructed geometry G t of the current point cloud frame and the motion compensated point cloud frame ⁇ G t p ,A ⁇ .
  • Some embodiments further include: generating an inter prediction G t p of a geometry of the current point cloud frame by applying the geometry-based motion information M geo to a geometry G t-1 of the reference point cloud frame; and determining a reconstructed geometry G t of the current point cloud frame based on the inter prediction G t p .
  • Some embodiments further comprise generating an inter prediction G t p of a geometry of the current point cloud frame by applying the geometry-based motion information M geo to a geometry G t-1 of the reference point cloud frame; wherein the reconstructed geometry G t of the current point cloud frame is based on the inter prediction G t p .
  • the attribute-based motion information 6M att consists of translation information. [0022] In some embodiments, the attribute-based motion information 6M att comprises translation information and rotation information.
  • the attribute-based motion information 6M att is determined by an exhaustive search within a search window.
  • the attribute-based motion information 6M att is determined using an iterative closest point technique.
  • a point cloud encoding method includes: determining geometry-based motion information M geo for a current point cloud frame; generating a first motion compensated prediction [Gf aeo , A v t aeo ] of the current point cloud frame based on the geometry-based motion information applied to a reference point cloud frame determining attribute-based motion information 6M att for the current point cloud frame; and predicting of attributes of the current point cloud frame based on the attributebased motion information 6M att applied to the first motion compensated prediction ⁇ G P fleo A p aeo
  • predicting attributes of the current point cloud frame comprises: generating a second motion compensated prediction ⁇ G P ,A P ⁇ of the current point cloud frame by applying, to the first motion compensated prediction ⁇ G p aeo ,A p aeo ⁇ , the attribute-based motion information 6M att determining a reconstructed geometry G t of the current point cloud frame; and predicting attributes of the current point cloud frame based on the reconstructed geometry G t of the current point cloud frame and the second motion compensated prediction [Gf,A p ],
  • Some embodiments further include generating a reconstructed geometry G t of the current point cloud frame based on a geometry G p aeo of the first motion compensated prediction of the current point cloud frame.
  • the attribute-based motion information 6M att consists of translation information.
  • the attribute-based motion information 6M att comprises translation information and rotation information.
  • the attribute-based motion information 6M att is determined by an exhaustive search within a search window.
  • the attribute-based motion information 6M att is determined using an iterative closest point technique.
  • Further embodiments include encoding and/or decoding apparatus comprising one or more processors configured to perform any of the methods described herein.
  • Further embodiments include a computer-readable medium (e.g. a non-transitory medium) including instructions for causing one or more processors to perform any of the methods described herein.
  • FIG. 1 A is a system diagram illustrating an example communications system in which one or more disclosed embodiments may be implemented.
  • FIG. 1 B 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 an embodiment.
  • WTRU wireless transmit/receive unit
  • FIG. 1C is a functional block diagram of a system used in some embodiments described herein.
  • FIG. 2 illustrates an example of a point cloud encoding scheme according to some embodiments.
  • FIG. 3 is a functional block diagram of a point cloud attribute encoder with attributebased motion information according to some embodiments.
  • FIG. 4 is a functional block diagram of a point cloud attribute decoder with attributebased motion information according to some embodiments.
  • FIG. 5 provides a schematic illustration of motion compensation of a point cloud segment from the reference frame.
  • FIG. 6 provides a schematic illustration of an example method of motion refinement using an exhaustive search of translational refinements within a search window.
  • FIG. 7 is a schematic illustration of features of a motion refinement using an iterative closest point (ICP) technique.
  • FIG. 8 is a functional block diagram of a point cloud attribute encoder with attributebased motion refinement.
  • FIG. 9 is a functional block diagram of a point cloud attribute decoder with attributebased motion refinement.
  • 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 zero-tail unique-word DFT-Spread OFDM
  • UW-OFDM unique word OFDM
  • FBMC filter bank multicarrier
  • the communications system 100 may include wireless transmit/receive units (WTRUs) 102a, 102b, 102c, 102d, a RAN 104, a ON 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 (loT) 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
  • HMD head-mounted display
  • a vehicle a drone
  • 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, 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. For example, 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 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, CDMA2000 1X, 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, CDMA2000 1X, CDMA2000 EV-DO Code Division Multiple Access 2000
  • IS-95 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 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 and/orthe ON 106 may be in direct or indirect communication with other RANs that employ the same RAT as the RAN 104 or a different RAT.
  • the ON 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/orthe 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 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. 1 B 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. While FIG. 1 B depicts the processor 118 and the transceiver 120 as separate components, it will be appreciated that the processor 118 and the transceiver 120 may be integrated together in an electronic package or chip.
  • 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.
  • 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 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 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 multimode 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.
  • dry cell batteries e.g., nickel-cadmium (NiCd), nickel-zinc (NiZn), nickel metal hydride (NiMH), lithium-ion (Li-ion), etc.
  • solar cells e.g., 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.
  • location information e.g., longitude and latitude
  • 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.
  • FM frequency modulated
  • 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.
  • 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 WRTU 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-1 B 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 block diagram of an example of a system in which various aspects and embodiments are implemented.
  • System 1000 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 1000, 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 1000 are distributed across multiple ICs and/or discrete components.
  • the system 1000 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 1000 is configured to implement one or more of the aspects described in this document.
  • the system 1000 includes at least one processor 1010 configured to execute instructions loaded therein for implementing, for example, the various aspects described in this document.
  • Processor 1010 can include embedded memory, input output interface, and various other circuitries as known in the art.
  • the system 1000 includes at least one memory 1020 (e.g., a volatile memory device, and/or a non-volatile memory device).
  • System 1000 includes a storage device 1040, 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 1040 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 1000 includes an encoder/decoder module 1030 configured, for example, to process data to provide an encoded video or decoded video, and the encoder/decoder module 1030 can include its own processor and memory.
  • the encoder/decoder module 1030 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 1030 can be implemented as a separate element of system 1000 or can be incorporated within processor 1010 as a combination of hardware and software as known to those skilled in the art.
  • Program code to be loaded onto processor 1010 or encoder/decoder 1030 to perform the various aspects described in this document can be stored in storage device 1040 and subsequently loaded onto memory 1020 for execution by processor 1010.
  • processor 1010, memory 1020, storage device 1040, and encoder/decoder module 1030 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 1010 and/or the encoder/decoder module 1030 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 1010 or the encoder/decoder module 1030) is used for one or more of these functions.
  • the external memory can be the memory 1020 and/or the storage device 1040, 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 WC (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
  • WC Very Video Coding
  • the input to the elements of system 1000 can be provided through various input devices as indicated in block 1130.
  • 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
  • the input devices of block 1130 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.
  • 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 1000 to other electronic devices across USB and/or HDMI connections.
  • 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 1010 as necessary.
  • aspects of USB or HDMI interface processing can be implemented within separate interface ICs or within processor 1010 as necessary.
  • the demodulated, error corrected, and demultiplexed stream is provided to various processing elements, including, for example, processor 1010, and encoder/decoder 1030 operating in combination with the memory and storage elements to process the datastream as necessary for presentation on an output device.
  • connection arrangement 1140 for example, an internal bus as known in the art, including the I nter-IC (I2C) bus, wiring, and printed circuit boards.
  • I2C I nter-IC
  • the system 1000 includes communication interface 1050 that enables communication with other devices via communication channel 1060.
  • the communication interface 1050 can include, but is not limited to, a transceiver configured to transmit and to receive data over communication channel 1060.
  • the communication interface 1050 can include, but is not limited to, a modem or network card and the communication channel 1060 can be implemented, for example, within a wired and/or a wireless medium.
  • Data is streamed, or otherwise provided, to the system 1000, 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).
  • the Wi-Fi signal of these embodiments is received over the communications channel 1060 and the communications interface 1050 which are adapted for Wi-Fi communications.
  • the communications channel 1060 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 1000 using a set-top box that delivers the data over the HDMI connection of the input block 1130.
  • Still other embodiments provide streamed data to the system 1000 using the RF connection of the input block 1130. As indicated above, various embodiments provide data in a non-streaming manner. Additionally, various embodiments use wireless networks other than Wi-Fi, for example a cellular network or a Bluetooth network.
  • the system 1000 can provide an output signal to various output devices, including a display 1100, speakers 1110, and other peripheral devices 1120.
  • the display 1100 of various embodiments includes one or more of, for example, a touchscreen display, an organic lightemitting diode (OLED) display, a curved display, and/or a foldable display.
  • the display 1100 can be for a television, a tablet, a laptop, a cell phone (mobile phone), or other device.
  • the display 1100 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 1120 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.
  • Various embodiments use one or more peripheral devices 1120 that provide a function based on the output of the system 1000. For example, a disk player performs the function of playing the output of the system 1000.
  • control signals are communicated between the system 1000 and the display 1100, speakers 1110, or other peripheral devices 1120 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 1000 via dedicated connections through respective interfaces 1070, 1080, and 1090. Alternatively, the output devices can be connected to system 1000 using the communications channel 1060 via the communications interface 1050.
  • the display 1100 and speakers 1110 can be integrated in a single unit with the other components of system 1000 in an electronic device such as, for example, a television.
  • the display interface 1070 includes a display driver, such as, for example, a timing controller (T Con) chip.
  • the display 1100 and speaker 1110 can alternatively be separate from one or more of the other components, for example, if the RF portion of input 1130 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 embodiments can be carried out by computer software implemented by the processor 1010 or by hardware, or by a combination of hardware and software. As a non- limiting example, the embodiments can be implemented by one or more integrated circuits.
  • the memory 1020 can be of any type appropriate to the technical environment and can be implemented using any appropriate data storage technology, such as optical memory devices, magnetic memory devices, semiconductor-based memory devices, fixed memory, and removable memory, as non-limiting examples.
  • the processor 1010 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.
  • Example embodiments provide systems and methods to integrate motion estimation and compensation in a global point cloud compression scheme with temporal prediction of both geometry and attributes.
  • Motion estimation is a processing block in compression schemes for point cloud attributes with inter-frame prediction. Motion estimation takes as input two successive point cloud frames PC t-1 (the reference frame) and PC t (the current frame) and outputs a 3D motion vector field M which describes the displacements of the points making the reference point cloud frame PC t-1 during time interval [t - 1, t] in order to generate a prediction of the current point cloud frame PC t .
  • the embodiments described herein are not limited to the use of any particular motion estimation algorithm.
  • Example embodiments described herein integrate a motion estimation processing block within a compression scheme for point cloud attributes with inter-frame prediction.
  • the present disclosure describes stage(s) of the encoding/decoding process in which motion estimation is performed, and it further describes examples of which input to use and with which cost function to minimize.
  • a point cloud frame PC includes a geometry component G, which may be described as
  • the attribute component represents the attributes of the points, for example their colour.
  • the number of points /V can change from frame to frame.
  • the motion information M from which the 3D motion vector field M is derived can be represented with various models, from simple translations per block with voxel accuracy to complex non-rigid deformation with sub-voxel accuracy per arbitrary-shaped clusters.
  • FIG. 2 illustrates an example of a point cloud encoding scheme according to some embodiments.
  • the geometry and attributes are sequentially compressed, as illustrated in FIG. 2.
  • the geometry is first compressed, then input attributes are transferred on the reconstructed geometry (after decompression) and compressed.
  • the reconstructed geometry is available when decoding the attributes.
  • the present disclosure describes systems and methods for attribute compression that may be used with various different techniques of geometry compression.
  • Inter-frame motion estimation is used to provide an accurate temporal prediction of the current point cloud frame to encode, both for its geometry component and its attributes.
  • One approach for motion estimation is to attempt to minimize the geometry prediction error dist( ⁇ G p , G t ⁇ ) where G t is the current point cloud frame, and G t p is the prediction of the point positions of the current point cloud frame obtained by displacing the points of previous frame along the motion vectors.
  • the cost function to be minimized by the motion estimation algorithm may be a classical rate distortion optimization criterion dist(G p , G ⁇ + R M) , where R(M) is an estimate of the bitrate of the motion information.
  • Another approach for motion estimation is to use a distortion function that combines the geometry and attribute prediction errors, e.g., dist(G p , + f>.
  • dist(A v t , A t where A t represents the point attributes of the current point cloud frame, and A p is the prediction of the point attributes of current point cloud frame obtained by displacing the points of previous frame along the motion vectors.
  • a t represents the point attributes of the current point cloud frame
  • a p is the prediction of the point attributes of current point cloud frame obtained by displacing the points of previous frame along the motion vectors.
  • Example embodiment disclosed herein operate to provide a good motion- compensated prediction of point cloud attributes, without degrading the geometry encoding performance or inflating the transmission cost of motion vectors within a compression scheme for dynamic point clouds.
  • example embodiments perform a refinement process of the motion information used to predict the point cloud attributes.
  • a refinement process may be performed as follows:
  • the 3D motion transform predicting the geometry initializes the estimation of a second motion transform for predicting the attributes.
  • the motion correction is searched for in an effort to minimize the attribute prediction error.
  • the motion correction is represented by a translational model.
  • the correction is represented by a rigid transform with a combination of translation and rotation.
  • the motion correction is transmitted to the decoder and composed with the decoded geometry-based motion transform to predict the attributes.
  • FIG. 3 is a functional block diagram of a point cloud attribute encoder according to some embodiments.
  • the embodiment depicted in FIG. 3 is built on top of a classical compression scheme for point cloud (geometry and attributes) with inter-frame prediction, for example the one described in “Test model for geometry-based solid point cloud - GeS TM 1.0,” ISO/IEC JTC1/SC29/WG7, 141st MPEG meeting, Online, Tech. Rep. N00558, January 2023.
  • An example of an embodiment of a corresponding decoder is depicted in FIG. 4. With regard to FIG.
  • the processing blocks that are focused on in the present disclosure are the blocks 302 “Motion refinement (attribute-based),” 304 “Motion encoding,” and 306 “Arithmetic coding.”
  • the processing blocks that are focused on in the present disclosure are the shaded blocks 402 “Arithmetic decoding,” 404 “Motion decoder,” and 406 “Motion compose.”
  • the motion information estimated to predict the geometry of the point cloud is refined at 302 before being used to temporally predict the attributes.
  • the motion refinement is encoded at 304 and 306 (e.g. using arithmetic coding) and multiplexed with the attribute bitstream.
  • the motion refinement component is decoded at 402 and 404 and composed at 406 with the motion information from the geometry decoder for use by the attribute decoder for motion compensated temporal prediction of attributes.
  • the embodiments of the present disclosure are not limited to any particular motion estimation algorithm or any particular motion information representation (the motion model), as long as a 3D motion vector field consisting of 3D displacement vectors assigned to every point of the reference point cloud frame can be derived from the model.
  • a motion model used with example embodiments may be represented as follows:
  • the reference point cloud frame is divided into N segments S k , k G [1,2V], with N k points each.
  • the segments are either delimited with non-overlapping 3D blocks or correspond to arbitrary-shaped clusters.
  • a parametrical 3D motion transform M k (.) is associated with each segment, which points p i G [1, N k ] project onto the current frame at position M k (p ).
  • the predicted positions can be with voxel accuracy or sub-voxel accuracy.
  • One example of such motion transform is a rigid transform composed of a 3D translation component T k and a 3D rotation component R k .
  • Another example consists of the translation component only.
  • the motion transform M aeo estimated between previous decoded (reference) and current input point cloud frames, on a geometry-only prediction error minimization criterion is used by the geometry encoder to provide a temporal prediction of current geometry.
  • This motion transform is used in some embodiments of the present disclosure to provide an initialization of a motion transform M att for predicting the attributes.
  • M att is estimated in some embodiments by obtaining a correction 6M att to M geo based on an attribute-only prediction error minimization criterion, such that
  • M att 6M att ° M aeo , where represents composition.
  • FIG. 5 provides a schematic illustration of motion compensation of a point cloud segment from the reference frame.
  • an attribute-based prediction error E(S k ,M k ) associated with motion M k (. ) is obtained as the average difference between attributes A ⁇ Cp ) of points p, in the decoded reference frame and attributes A t (p-) of their corresponding points p- in the current frame.
  • the corresponding point may be determined as the closest one to the predicted position according the motion transform.
  • the attribute-based prediction error may be obtained through calculations represented by equations (1) and (2) using techniques illustrated in FIG. 5.
  • a translational refinement is performed using an exhaustive search.
  • a refinement of rigid rotation and translation is performed using an iterative closest point technique. Additional refinement techniques may alternatively be used.
  • refinement using rigid rotation and translation is performed using an iterative closest point (ICP) technique.
  • ICP iterative closest point
  • a combination of rotation and translation refinement 6M k H is determined by running the iterative closest point technique, modified to take into account the attribute prediction error. This process is described by equation (4) and is schematically illustrated in FIG. 7.
  • predicted position M ⁇ eo pi is calculated using the motion field of the geometry. For each of those points Pi, a corresponding matched point p is selected from among points in the current frame G t , where the matched point p t ' is the nearest in attribute distance from point p L from among the points in G t that are within a spatial radius T from the location M ⁇ eo pi).
  • FIG. 7 illustrates an example in which changing two matched pairs of neighbours from a geometric to an attribute distance enables estimation of a rotation increment of the segment motion with a better prediction of attributes.
  • this or any other embodiment described herein may be modified to allow for approximations of features disclosed herein, whether to reduce computational complexity or for other reasons.
  • such points may be selected from within a cube-shaped neighborhood (e.g. a window of size A x A x A centered on M k eo .Pi)
  • a sum of absolute differences may be minimized in other embodiments.
  • a feature is described as determining a minimum (or maximum) value, this should be understood to include algorithms configured to minimize (or maximize) a value within practical constraints, such as within a particular number of operations, or within a particular search window, as the determination of a true global minimum (or maximum) can be computationally expensive.
  • signalling is provided to indicate to decoders whether or not the attribute bitstream contains a motion refinement component.
  • encoders that are able to perform such motion refinement may decide not to perform it when unnecessary, on a frame or a slice of frame basis.
  • some embodiments modify the G-PCC bitstream syntax by providing a binary flag for that purpose to the attribute data unit (ADU) header of each slice of the point cloud.
  • ADU attribute data unit
  • the motion transform M geo for geometry prediction may be encoded, multiplexed with the encoded geometry into the geometry bitstream, and transmitted to the decoder using conventional techniques.
  • the motion transform is therefore available when decoding the attributes.
  • example embodiments further provide for the encoding of the motion refinement increment 6M att and multiplexing of that refinement with the encoded attributes into the attribute bitstream.
  • encode 6 (3 x , 3 y , 3 z ) 3D vector updates.
  • 5 > 1 may be entropy coded using context updates. Values greater than 2 may be coded with an Exp-Golomb binarization in bypass mode. And the sign of the motion difference may be coded as a flag in bypass mode as well.
  • a quaternion representation may be quantized and encoded.
  • only three components Qx, Qy qnd Qz are transmitted as the fourth one Qw may be deduced as follows: [0124]
  • the quaternion components may be coded using a signed integer with 32 bits and may be restricted to be in the range of -2 30 to 2 30 .
  • the motion refinement part 8M att may be decoded from the attribute bitstream and composed with the motion information used by the geometry decoder M aeo .
  • a reconstructed geometry G t is determined for the current point cloud frame, and attributes of the points in the reconstructed geometry are predicted from the motion compensated point cloud frame
  • the prediction may be performed using any of a variety of prediction techniques, such as predicting an attribute of a point in G t using an attribute of the nearest point in or predicting an attribute of a point in G t based on a weighted (or otherwise filtered) combination of attributes of nearby points in (G t p , A v t .
  • some embodiments use the motion field of the geometry as a reference for predicting the attribute motion.
  • the predicted geometry is used as a reference for predicting the attribute motion. Examples of the latter embodiments are illustrated with reference to FIGs. 8 and 9.
  • FIG. 8 is a functional block diagram of a point cloud attribute encoder with attributebased motion refinement.
  • a current point cloud frame is represented by PC t , which includes both geometry information G t and attribute information A t .
  • Geometry-based motion information M geo for the current point cloud frame G t relative to geometry G t-1 of a reference point cloud frame is determined at 802.
  • the reference point cloud frame may be a reconstructed reference frame stored in a frame memory 806.
  • a first motion compensated prediction PC a aeo of the current point cloud frame is generated based on the geometry-based motion information M geo applied to both the geometry G t-1 and the attributes A t-1 of the reference point cloud frame.
  • attribute-based motion information 6M att is determined for the current point cloud frame PC t relative to the first motion compensated prediction PC a aeo . Attributes of the current point cloud frame are predicted at 810 based on the attribute-based motion information 8M att applied to the first motion compensated prediction.
  • predicting attributes of the current point cloud frame is performed by generating a second motion compensated prediction of the current point cloud frame by applying the attribute-based motion information 6M att to the first motion compensated prediction PC t r ’ 3eo , determining a reconstructed geometry of the current point cloud frame, and predicting attributes of the current point cloud frame based on the reconstructed geometry of the current point cloud frame and the second motion compensated prediction.
  • Some embodiments further include generating a reconstructed geometry of the current point cloud frame based on a geometry of the first motion compensated prediction of the current point cloud frame.
  • the attribute-based motion information includes translation information and does not include rotation information. In other embodiments, the attributebased motion information includes both translation information and rotation information.
  • the attribute-based motion information is determined by an exhaustive search within a search window. In other embodiments, the attribute-based motion information is determined using an iterative closest point technique.
  • FIG. 9 is a functional block diagram of a point cloud attribute decoder with attributebased motion refinement.
  • geometrybased motion information is obtained by a motion decoder 902 from a geometry bitstream for a current point cloud frame.
  • a first motion compensated prediction of the current point cloud frame is generated at 904 based on the geometry-based motion information applied to a reference point cloud frame.
  • Attribute-based motion information 6M att for the current point cloud frame is obtained at 906 from an attribute bitstream. Attributes of the current point cloud frame are predicted at 908 based on the attribute-based motion information applied to the first motion compensated prediction.
  • predicting attributes of the current point cloud frame is performed by generating a second motion compensated prediction of the current point cloud frame by applying the attribute-based motion information to the first motion compensated prediction, determining a reconstructed geometry of the current point cloud frame, and predicting attributes of the current point cloud frame based on the reconstructed geometry of the current point cloud frame and the second motion compensated prediction.
  • Some embodiment include generating a reconstructed geometry G t of the current point cloud frame based on a geometry G a aeo of the first motion compensated prediction of the current point cloud frame.
  • the attribute-based motion information includes translation information and does not include rotation information. In other embodiments, the attributebased motion information includes both translation information and rotation information.
  • the motion refinement processing block takes as input the point cloud temporal prediction already motion compensated with the initial motion transform computed from geometry M geo .
  • the geometry component G a aeo ⁇ s already computed within the geometry encoder, and the output is completed with the displaced attribute component A a aeo .
  • the motion compensated prediction for the encoding of attributes, at both encoder and decoder, also takes as input this first motion compensated point cloud and compensates it using the motion refinement 8M att only, without having to compose it with M geo .
  • example embodiments provide for a motion-compensated inter-frame prediction of point cloud attributes that may be performed with higher precision, reducing the amplitude of the attribute residue to encode and therefore the bit-rate of the attribute bitstream. This may be achieved at the expense of an increased motion data bit-rate (to transmit the motion field refinement). This increase in bit-rate, however, may be quite limited due to the restricted search range.
  • a point cloud decoding method comprises: obtaining geometry-based motion information M geo for a current point cloud frame; obtaining attribute-based motion information 6M att for the current point cloud frame; and predicting attributes of the current point cloud frame based on a composition 6M att ° M geo of the geometry-based motion information and the attribute-based motion information applied to a reference point cloud frame [0142]
  • predicting attributes of the current point cloud frame comprises: generating a motion compensated point cloud frame ⁇ Gf, f ⁇ by applying, to the reference point cloud frame ⁇ (?(_!, the composition 6M att ° M geo of the geometry-based motion information and the attribute-based motion information; generating a reconstructed geometry G t of the current point cloud frame; and predicting attributes of the current point cloud frame based on the reconstructed geometry G t of the current point cloud frame and the motion compensated point cloud frame ⁇ Gf, f ⁇ -
  • Some embodiments further comprise: generating an inter prediction Gf of a geometry of the current point cloud frame by applying the geometry-based motion information M geo to a geometry G t-1 of the reference point cloud frame; and determining a reconstructed geometry G t of the current point cloud frame based on the inter prediction Gf .
  • Some embodiments further comprise generating an inter prediction Gf of a geometry of the current point cloud frame by applying the geometry-based motion information M geo to a geometry G t-1 of the reference point cloud frame; wherein the reconstructed geometry G t of the current point cloud frame is based on the inter prediction Gf .
  • the attribute-based motion information 6M att consists of translation information without rotation information.
  • the attribute-based motion information 6M att comprises translation information and rotation information.
  • a point cloud decoding method comprises: obtaining geometry-based motion information M geo for a current point cloud frame; generating a first motion compensated prediction ⁇ G aeo ,A p t aeo ] of the current point cloud frame based on the geometry-based motion information applied to a reference point cloud frame obtaining attribute-based motion information 6M att for the current point cloud frame; and predicting of attributes of the current point cloud frame based on the attributebased motion information 6M att applied to the first motion compensated prediction ⁇ G P fleo A p aeo
  • predicting attributes of the current point cloud frame comprises: generating a second motion compensated prediction ⁇ G P ,A P ⁇ of the current point cloud frame by applying, to the first motion compensated prediction ⁇ G p aeo ,A p aeo ⁇ , the attribute-based motion information 6M att determining a reconstructed geometry G t of the current point cloud frame; and predicting attributes of the current point cloud frame based on the reconstructed geometry G t of the current point cloud frame and the second motion compensated prediction ⁇ G ,A ⁇ .
  • Some embodiments further comprise: generating a reconstructed geometry G t of the current point cloud frame based on a geometry Gf aeo of the first motion compensated prediction of the current point cloud frame.
  • a point cloud encoding method comprises: determining geometry-based motion information M geo for a current point cloud frame; determining attribute-based motion information 6M att for the current point cloud frame; and predicting attributes of the current point cloud frame based on a composition 6M att ° M geo of the geometry-based motion information and the attribute-based motion information applied to a reference point cloud frame
  • predicting attributes of the current point cloud frame comprises: generating a motion compensated point cloud frame [G t r ’,A r ’] by applying, to the reference point cloud frame ⁇ ?(_!, A t-t ⁇ , the composition 6M att ° M geo of the geometry-based motion information and the attribute-based motion information; generating a reconstructed geometry G t of the current point cloud frame; and predicting attributes of the current point cloud frame based on the reconstructed geometry G t of the current point cloud frame and the motion compensated point cloud frame ⁇ G ,A ⁇ .
  • Some embodiments further comprise: generating an inter prediction G t p of a geometry of the current point cloud frame by applying the geometry-based motion information M geo to a geometry G t-1 of the reference point cloud frame; and determining a reconstructed geometry G t of the current point cloud frame based on the inter prediction G t p .
  • Some embodiments further comprising generating an inter prediction G t p of a geometry of the current point cloud frame by applying the geometry-based motion information M geo to a geometry G t-1 of the reference point cloud frame; wherein the reconstructed geometry G t of the current point cloud frame is based on the inter prediction G t p .
  • the attribute-based motion information 6M att is determined by an exhaustive search within a search window.
  • the attribute-based motion information 6M att is determined using an iterative closest point technique.
  • a point cloud encoding method comprises: determining geometry-based motion information M geo for a current point cloud frame; generating a first motion compensated prediction [Gf aeo , A v t geo of the current point cloud frame based on the geometry-based motion information applied to a reference point cloud frame determining attribute-based motion information 6M att for the current point cloud frame; and predicting of attributes of the current point cloud frame based on the attributebased motion information 6M att applied to the first motion compensated prediction ⁇ G P fleo A P 3eo
  • predicting attributes of the current point cloud frame comprises: generating a second motion compensated prediction ⁇ G p ,A p ⁇ of the current point cloud frame by applying, to the first motion compensated prediction [Gf aeo ,A p aeo ] , the attribute-based motion information 6M att determining a reconstructed geometry G t of the current point cloud frame; and predicting attributes of the current point cloud frame based on the reconstructed geometry G t of the current point cloud frame and the second motion compensated prediction [Gf,A p ],
  • Some embodiments further include generating a reconstructed geometry G t of the current point cloud frame based on a geometry G p aeo of the first motion compensated prediction of the current point cloud frame.
  • a point cloud decoding apparatus comprises one or more processors configured to perform at least: obtaining geometry-based motion information M geo for a current point cloud frame; obtaining attribute-based motion information 6M att for the current point cloud frame; and predicting attributes of the current point cloud frame based on a composition 6M att ° M geo of the geometry-based motion information and the attributebased motion information applied to a reference point cloud frame
  • predicting attributes of the current point cloud frame comprises: generating a motion compensated point cloud frame ⁇ G p ,A p ⁇ by applying, to the reference point cloud frame ⁇ ?(_!, A t-t ⁇ , the composition 6M att ° M geo of the geometry-based motion information and the attribute-based motion information; generating a reconstructed geometry G t of the current point cloud frame; and predicting attributes of the current point cloud frame based on the reconstructed geometry G t of the current point cloud frame and the motion compensated point cloud frame [G p ,A p ],
  • Some embodiments further comprise: generating an inter prediction G p of a geometry of the current point cloud frame by applying the geometry-based motion information M geo to a geometry G t-1 of the reference point cloud frame; and determining a reconstructed geometry G t of the current point cloud frame based on the inter prediction G p .
  • Some embodiments further comprise generating an inter prediction G t p of a geometry of the current point cloud frame by applying the geometry-based motion information M geo to a geometry G t-1 of the reference point cloud frame; wherein the reconstructed geometry G t of the current point cloud frame is based on the inter prediction G t p .
  • a point cloud decoding apparatus comprises one or more processors configured to perform at least: obtaining geometry-based motion information M geo for a current point cloud frame; generating a first motion compensated prediction [Gf aeo ,A a aeo ] of the current point cloud frame based on the geometry-based motion information applied to a reference point cloud frame ⁇ (?(_!, ⁇ ; obtaining attribute-based motion information 8M att for the current point cloud frame; and predicting of attributes of the current point cloud frame based on the attribute-based motion information 8M att applied to the first motion compensated prediction ⁇ G a aeo ,A a aeo ⁇ .
  • predicting attributes of the current point cloud frame comprises: generating a second motion compensated prediction ⁇ G a ,A a ⁇ of the current point cloud frame by applying, to the first motion compensated prediction ⁇ G a aeo ,A a aeo ], the attribute-based motion information 8M att determining a reconstructed geometry G t of the current point cloud frame; and predicting attributes of the current point cloud frame based on the reconstructed geometry G t of the current point cloud frame and the second motion compensated prediction [Gf,A a ],
  • Some embodiments further include: generating a reconstructed geometry G t of the current point cloud frame based on a geometry G a aeo of the first motion compensated prediction of the current point cloud frame.
  • a point cloud encoding apparatus comprises one or more processors configured to perform at least: determining geometry-based motion information M geo for a current point cloud frame; determining attribute-based motion information 8M att for the current point cloud frame; and predicting attributes of the current point cloud frame based on a composition 8M att ° M geo of the geometry-based motion information and the attribute-based motion information applied to a reference point cloud frame ⁇ G ⁇ t-i ⁇ .
  • predicting attributes of the current point cloud frame comprises: generating a motion compensated point cloud frame ⁇ G t r ’,A r ’ ⁇ by applying, to the reference point cloud frame ⁇ ?(_!, A t-t ⁇ , the composition 8M att ° M geo of the geometry-based motion information and the attribute-based motion information; generating a reconstructed geometry G t of the current point cloud frame; and predicting attributes of the current point cloud frame based on the reconstructed geometry G t of the current point cloud frame and the motion compensated point cloud frame ⁇ G ,A ⁇ .
  • Some embodiments include: generating an inter prediction G t p of a geometry of the current point cloud frame by applying the geometry-based motion information M geo to a geometry G t-1 of the reference point cloud frame; and determining a reconstructed geometry G t of the current point cloud frame based on the inter prediction G t p .
  • Some embodiments further comprise generating an inter prediction G t p of a geometry of the current point cloud frame by applying the geometry-based motion information M geo to a geometry G t-1 of the reference point cloud frame; wherein the reconstructed geometry G t of the current point cloud frame is based on the inter prediction G t p .
  • a point cloud encoding apparatus comprises one or more processors configured to perform at least: determining geometry-based motion information M geo for a current point cloud frame; generating a first motion compensated prediction ⁇ G t P fleo ,Af geo ] of the current point cloud frame based on the geometry-based motion information applied to a reference point cloud frame determining attributebased motion information 6M att for the current point cloud frame; and predicting of attributes of the current point cloud frame based on the attribute-based motion information 6M att applied to the first motion compensated prediction [G a aeo , A v t aeo ⁇ .
  • predicting attributes of the current point cloud frame comprises: generating a second motion compensated prediction ⁇ G a ,A a ⁇ of the current point cloud frame by applying, to the first motion compensated prediction [Gf aeo ,A a aeo ] , the attribute-based motion information 6M att determining a reconstructed geometry G t of the current point cloud frame; and predicting attributes of the current point cloud frame based on the reconstructed geometry G t of the current point cloud frame and the second motion compensated prediction [Gf,A a ],
  • Some embodiments further comprise: generating a reconstructed geometry G t of the current point cloud frame based on a geometry G a aeo of the first motion compensated prediction of the current point cloud frame.
  • Further embodiments include a computer-readable medium (e.g. a non-transitory medium) storing a point cloud encoded using any of the methods described herein. [0175] Further embodiments include a signal representing a point cloud encoded using any of the methods described herein.
  • a computer-readable medium e.g. a non-transitory medium
  • This disclosure describes a variety of aspects, including tools, features, embodiments, models, approaches, etc. Many of these aspects are described with specificity and, at least to show the individual characteristics, are often described in a manner that may sound limiting. However, this is for purposes of clarity in description, and does not limit the disclosure or scope of those aspects. Indeed, all of the different aspects can be combined and interchanged to provide further aspects. Moreover, the aspects can be combined and interchanged with aspects described in earlier filings as well.
  • At least one of the aspects generally relates to point cloud encoding and decoding, and at least one other aspect generally relates to transmitting a bitstream generated or encoded.
  • At least one of the aspects generally relates to point cloud encoding and decoding, and at least one other aspect generally relates to transmitting a bitstream generated or encoded.
  • These and other aspects can be implemented as a method, an apparatus, a computer readable storage medium having stored thereon instructions for encoding or decoding point cloud data according to any of the methods described, and/or a computer readable storage medium having stored thereon a bitstream generated according to any of the methods described.
  • 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. Additionally, terms such as “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.
  • 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.
  • Decoding 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.
  • 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.
  • encoding can encompass all or part of the processes performed, for example, on an input video sequence in order to produce an encoded bitstream.
  • processes include one or more of the processes typically performed by an encoder, for example, partitioning, differential encoding, transformation, quantization, and entropy encoding.
  • 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.
  • Various embodiments refer to rate distortion optimization.
  • 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.
  • 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. More generally, many approaches employ any of a variety of techniques to perform the optimization, but the optimization is not necessarily a complete evaluation of both the coding cost and related distortion.
  • the implementations and aspects described herein can be implemented in, for example, a method or a process, an apparatus, a software program, a data stream, or a signal. Even if only discussed in the context of a single form of implementation (for example, discussed only as a method), the implementation of features discussed can also be implemented in other forms (for example, an apparatus or program).
  • An apparatus can be implemented in, for example, appropriate hardware, software, and firmware.
  • the methods can be implemented in, for example, 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
  • references to “one embodiment” or “an embodiment” or “one implementation” or “an implementation”, as well as other variations thereof, means that a particular feature, structure, characteristic, and so forth described in connection with the embodiment is included in at least one embodiment.
  • the appearances of the phrase “in one embodiment” or “in an embodiment” or “in one implementation” or “in an implementation”, as well any other variations, appearing in various places throughout this disclosure are not necessarily all referring to the same embodiment.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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

L'invention concerne des systèmes et des procédés de codage et de décodage de nuage de points à l'aide d'informations d'affinement de mouvement basées sur des attributs pour une prédiction inter d'attributs. Dans un exemple de procédé de décodage, des informations de mouvement basées sur la géométrie Mgeo sont obtenues pour une trame de nuage de points courante. Une première prédiction à compensation de mouvement de la trame de nuage de points courante est générée sur la base des informations de mouvement basées sur la géométrie appliquées à une trame de nuage de points de référence. Des informations de mouvement basées sur des attributs sont obtenues pour la trame de nuage de points courante. Des attributs de la trame de nuage de points courante sont prédits sur la base des informations de mouvement basées sur des attributs appliquées à la première prédiction à compensation de mouvement.
PCT/EP2024/067887 2023-07-10 2024-06-26 Correction de mouvement pour prédiction d'attributs de nuage de points Pending WO2025011956A1 (fr)

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Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
D. GRAZIOSI ET AL.: "An overview of ongoing point cloud compression standardization activities: video-based (V-PCC) and geometry-based (G-PCC", APSIPA TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING, vol. 9, no. 1, 2020, pages e13, XP093012170, DOI: 10.1017/ATSIP.2020.12
HONG HAORAN ET AL: "Motion Estimation And Filtered Prediction For Dynamic Point Cloud Attribute Compression", 2022 PICTURE CODING SYMPOSIUM (PCS), IEEE, 7 December 2022 (2022-12-07), pages 139 - 143, XP034279328, DOI: 10.1109/PCS56426.2022.10018071 *
LI LI ET AL: "Advanced 3D Motion Prediction for Video Based Point Cloud Attributes Compression", 2019 DATA COMPRESSION CONFERENCE (DCC), IEEE, 26 March 2019 (2019-03-26), pages 498 - 507, XP033548535, DOI: 10.1109/DCC.2019.00058 *
YIQUN XU ET AL: "Predictive Generalized Graph Fourier Transform for Attribute Compression of Dynamic Point Clouds", ARXIV.ORG, CORNELL UNIVERSITY LIBRARY, 201 OLIN LIBRARY CORNELL UNIVERSITY ITHACA, NY 14853, 6 August 2019 (2019-08-06), XP081456426 *

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