Motion correction for point cloud attribute prediction

Abstract

Systems and methods for point cloud encoding and decoding using attribute-based motion improvement information for inter prediction of attributes. In an example decoding method, geometry-based motion information Meo is obtained for a current point cloud frame. A first motion compensated prediction for the current point cloud frame is generated based on geometry-based motion information applied to a reference point cloud frame. Attribute-based motion information is obtained for the current point cloud frame. An attribute of the current point cloud frame is predicted based on attribute-based motion information applied to the first motion compensation prediction.

Description

[0001] Cross-references This application claims priority to European Patent Application No. 23306168.8, filed on July 10, 2023, entitled “Motion Correction for PointCloud Attributes Prediction,” which is incorporated herein by reference in its entirety. Background Technology

[0002] Advances in 3D capture and rendering technologies are enabling new applications and services in areas such as autonomous driving, cultural heritage archiving, immersive telepresence, and virtual / augmented reality. Point clouds have emerged as one of the primary 3D scene representations for such applications. A point cloud frame consists of a collection of 3D points, each represented using its 3D location and possibly several attributes such as color, transparency, and reflectivity.

[0003] Standardization activities for point cloud compression are being implemented 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 pointcloud compression standardization activities: video-based (V-PCC) and geometry-based (G-PCC)," APSIPA Transactions on Signal and Information Processing, Volume 9, No. 1, e13, 2020. The first version of the geometry-based point cloud compression standard (G-PCC) is under development—part 9 of the ISO / IEC 23090 series on the encoded representation of immersive media.

[0004] Within the framework of G-PCC version 2, compression of dense dynamic point clouds using a geometry-based approach has been identified as a separate objective. The geometry-based approach performs compression in the 3D spatial domain without requiring 3D-to-2D round trips to utilize existing 2D video codecs. Current G-PCC encoders for dense dynamic point clouds leverage spatial (intra-frame) and temporal (inter-frame) redundancy to improve geometry compression performance.

[0005] Various 3D motion estimation algorithms can be used in point cloud compression schemes with motion-compensated inter-frame prediction, such as those based on Iterative Closest Point (ICP), block matching, or graph matching. Summary of the Invention

[0006] A point cloud decoding method according to some embodiments includes: obtaining geometry-based motion information for the current point cloud frame. Obtain attribute-based motion information for the current point cloud frame. ; and based on the reference point cloud frame Synthesis of geometry-based motion information and attribute-based motion information This is used to predict the attributes of the current point cloud frame.

[0007] In some embodiments, predicting the properties of the current point cloud frame includes: by analyzing the reference point cloud frame. The synthesis of the geometry-based motion information and the attribute-based motion information is applied. To generate motion-compensated point cloud frames. Generate the reconstructed geometry of the current point cloud frame. ; and the reconstructed geometry based on the current point cloud frame. Point cloud frames with motion compensation This is used to predict the attributes of the current point cloud frame.

[0008] Some embodiments further include: using the geometry-based motion information Geometric structure applied to the reference point cloud frame To generate inter-frame predictions of the geometry of the current point cloud frame. ; and based on the inter-frame prediction To determine the reconstructed geometry of the current point cloud frame. .

[0009] Some embodiments further include: using the geometry-based motion information Geometric structure applied to the reference point cloud frame To generate inter-frame predictions of the geometry of the current point cloud frame. The reconstructed geometry of the current point cloud frame; Based on the inter-frame prediction .

[0010] In some embodiments, the attribute-based motion information It consists of translation information.

[0011] In some embodiments, the attribute-based motion information This includes translation and rotation information.

[0012] A point cloud decoding method according to some embodiments includes: obtaining geometry-based motion information for the current point cloud frame. Based on the reference point cloud frame Based on geometric motion information, a first motion compensation prediction for the current point cloud frame is generated. Obtain attribute-based motion information for the current point cloud frame. ; and based on the motion compensation prediction applied to the first motion compensation prediction Attribute-based motion information This is used to predict the attributes of the current point cloud frame.

[0013] In some embodiments, predicting the properties of the current point cloud frame includes: predicting the properties of the first motion compensation frame. Applying the attribute-based motion information To generate the second motion compensation prediction for the current point cloud frame. Determine the reconstructed geometry of the current point cloud frame. ; and the reconstructed geometry based on the current point cloud frame. and the second motion compensation prediction This is used to predict the attributes of the current point cloud frame.

[0014] Some embodiments further include: a geometry predicted by a first motion compensation based on the current point cloud frame. To generate the reconstructed geometry of the current point cloud frame. .

[0015] In some embodiments, the attribute-based motion information It consists of translation information.

[0016] In some embodiments, the attribute-based motion information This includes translation and rotation information.

[0017] A point cloud encoding method according to some embodiments includes: determining geometry-based motion information for a current point cloud frame. Determine attribute-based motion information for the current point cloud frame. ; and based on the reference point cloud frame Synthesis of geometry-based motion information and attribute-based motion information This is used to predict the attributes of the current point cloud frame.

[0018] In some embodiments, predicting the properties of the current point cloud frame includes: by analyzing the reference point cloud frame. The synthesis of the geometry-based motion information and the attribute-based motion information is applied. To generate motion-compensated point cloud frames. Generate the reconstructed geometry of the current point cloud frame. ; and the reconstructed geometry based on the current point cloud frame. Point cloud frames with motion compensation This is used to predict the attributes of the current point cloud frame.

[0019] Some embodiments further include: using the geometry-based motion information Geometric structure applied to the reference point cloud frame To generate inter-frame predictions of the geometry of the current point cloud frame. ; and based on the inter-frame prediction To determine the reconstructed geometry of the current point cloud frame. .

[0020] Some embodiments further include: using the geometry-based motion information Geometric structure applied to the reference point cloud frame To generate inter-frame predictions of the geometry of the current point cloud frame. The reconstructed geometry of the current point cloud frame; Based on the inter-frame prediction .

[0021] In some embodiments, the attribute-based motion information It consists of translation information.

[0022] In some embodiments, the attribute-based motion information This includes translation and rotation information.

[0023] In some embodiments, the attribute-based motion information Determined by exhaustive search within the search window.

[0024] In some embodiments, the attribute-based motion information It was determined using the iterative nearest point technique.

[0025] A point cloud encoding method according to some embodiments includes: determining geometry-based motion information for a current point cloud frame. Based on the reference point cloud frame Based on geometric motion information, a first motion compensation prediction for the current point cloud frame is generated. Determine attribute-based motion information for the current point cloud frame. ; and based on the motion compensation prediction applied to the first motion compensation prediction Attribute-based motion information This is used to predict the attributes of the current point cloud frame.

[0026] In some embodiments, predicting the properties of the current point cloud frame includes: predicting the properties of the first motion compensation frame. Applying the attribute-based motion information To generate the second motion compensation prediction for the current point cloud frame. Determine the reconstructed geometry of the current point cloud frame. ; and the reconstructed geometry based on the current point cloud frame. and the second motion compensation prediction This is used to predict the attributes of the current point cloud frame.

[0027] Some embodiments further include: a geometry predicted by a first motion compensation based on the current point cloud frame. To generate the reconstructed geometry of the current point cloud frame. .

[0028] In some embodiments, the attribute-based motion information It consists of translation information.

[0029] In some embodiments, the attribute-based motion information This includes translation and rotation information.

[0030] In some embodiments, the attribute-based motion information Determined by exhaustive search within the search window.

[0031] In some embodiments, the attribute-based motion information It was determined using the iterative nearest point technique.

[0032] Further embodiments include encoding and / or decoding means comprising one or more processors configured to perform any of the methods described herein.

[0033] Further embodiments include a computer-readable medium (e.g., a non-transient medium) comprising instructions for causing one or more processors to perform any of the methods described herein.

[0034] Further embodiments include a computer-readable medium (e.g., a non-transient medium) that stores a point cloud encoded using any of the methods described herein.

[0035] Further embodiments include a signal representing a point cloud encoded using any of the methods described herein.

[0036] Further embodiments include a computer program product including instructions that, when executed by one or more processors, cause the one or more processors to perform any of the methods described herein. Attached Figure Description

[0037] Figure 1A This is a system diagram illustrating an example communication system in which one or more of the disclosed embodiments may be implemented.

[0038] Figure 1B The illustration shows a device that can be used according to one embodiment. Figure 1A The diagram shows a system diagram of an example wireless transmit / receive unit (WTRU) used in a communication system.

[0039] Figure 1C This is a functional block diagram of the system used in some of the embodiments described herein.

[0040] Figure 2 An example of a point cloud encoding scheme according to some embodiments is illustrated.

[0041] Figure 3 This is a functional block diagram of a point cloud attribute encoder with attribute-based motion information according to some embodiments.

[0042] Figure 4 This is a functional block diagram of a point cloud attribute decoder with attribute-based motion information according to some embodiments.

[0043] Figure 5 A schematic diagram of motion compensation for point cloud segments from a reference frame is provided.

[0044] Figure 6 A schematic diagram of an example method for motion improvement of exhaustive search using translation within the search window is provided.

[0045] Figure 7 This is a schematic diagram illustrating the motion improvement features using the Iterative Closest Point (ICP) technique.

[0046] Figure 8 This is a functional block diagram of a point cloud attribute encoder with attribute-based motion improvement.

[0047] Figure 9 This is a functional block diagram of a point cloud attribute decoder with attribute-based motion improvement. Detailed Implementation

[0048] Figure 1AThis diagram illustrates an example communication system 100 in which one or more of the disclosed embodiments may be implemented. The communication system 100 may be a multiple access system that provides content (such as voice, data, video, messaging, broadcasting, etc.) to multiple wireless users. The communication system 100 enables multiple wireless users to access such content by sharing system resources (including wireless bandwidth). For example, the communication system 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 Extended OFDM (ZT UW DTS-s OFDM), Unique Word OFDM (UW-OFDM), Resource Block Filtered OFDM, Filter Bank Multicarrier (FBMC), etc.

[0049] like Figure 1A As shown, the communication system 100 may include wireless transmit / receive units (WTRUs) 102a, 102b, 102c, 102d, RAN 104, CN 106, Public Switched Telephone Network (PSTN) 108, Internet 110, and other networks 112. Although it will be appreciated, the disclosed embodiments contemplate any number of WTRUs, base stations, networks, and / or network elements. Each of the WTRUs 102a, 102b, 102c, 102d can be any type of device configured to operate and / or communicate in a wireless environment. As an example, WTRUs 102a, 102b, 102c, and 102d (any of which may be referred to as a “station” and / or “STA”) may be configured to transmit and / or receive wireless signals and may include user equipment (UE), mobile stations, fixed or mobile subscriber units, subscription-based units, pagers, cellular phones, personal digital assistants (PDAs), smartphones, laptops, netbooks, personal computers, wireless sensors, hotspots or Mi-Fi devices, Internet of Things (IoT) devices, watches or other wearable devices, head-mounted displays (HMDs), vehicles, drones, medical devices and applications (e.g., remote surgery), industrial devices and applications (e.g., robots and / or other wireless devices operating in industrial and / or automated processing chain scenarios), consumer electronics devices, devices operating on commercial and / or industrial wireless networks, etc. Any of WTRUs 102a, 102b, 102c, and 102d may be interchangeably referred to as a UE.

[0050] The communication system 100 may also include base station 114a and / or base station 114b. Each of base stations 114a and 114b may be any type of device configured to wirelessly interface with at least one of WTRUs 102a, 102b, 102c, and 102d to facilitate access to one or more communication networks, such as CN 106, the Internet 110, and / or other networks 112. As an example, base stations 114a and 114b may be any of a base transceiver station (BTS), Node-B, eNode B, home node B, home eNode B, gNB, NR NodeB, site controller, access point (AP), wireless router, etc. Although base stations 114a and 114b are depicted as single elements, it will be understood that base stations 114a and 114b may include any number of interconnected base stations and / or network elements.

[0051] Base station 114a may be part of RAN 104 / 113, which may also include other base stations and / or network elements (not shown), such as base station controllers (BSCs), radio network controllers (RNCs), relay nodes, etc. Base station 114a and / or base station 114b may be configured to transmit and / or receive radio signals on one or more carrier frequencies, which may be referred to as cells (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 specific geographic area for a radio service, which may be relatively fixed or may change over time. A cell may be further divided into cell sectors. For example, the cell associated with base station 114a may be divided into three sectors. Therefore, in one embodiment, base station 114a may include three transceivers, i.e., one transceiver for each sector of the cell. In one embodiment, base station 114a may employ multiple-input multiple-output (MIMO) technology, and multiple transceivers may be used for each sector of the cell. For example, beamforming can be used to transmit and / or receive signals in a desired spatial direction.

[0052] Base stations 114a and 114b can communicate with one or more of WTRUs 102a, 102b, 102c, and 102d via air interface 116, which can be any suitable wireless communication link (e.g., radio frequency (RF), microwave, centimeter wave, millimeter wave, infrared (IR), ultraviolet (UV), visible light, etc.). Air interface 116 can be established using any suitable radio access technology (RAT).

[0053] More specifically, as noted above, the communication system 100 can be a multiple access system and can employ one or more channel access schemes, such as CDMA, TDMA, FDMA, OFDMA, SC-FDMA, etc. For example, base station 114a in RAN 104, and WTRUs 102a, 102b, and 102c can implement radio technologies such as Universal Mobile Telecommunications System (UMTS) Terrestrial Radio Access (UTRA), which can use Wideband CDMA (WCDMA) to establish the air interface 116. WCDMA can include communication protocols such as High-Speed ​​Packet Access (HSPA) and / or Evolved HSPA (HSPA+). HSPA can include High-Speed ​​Downlink (DL) Packet Access (HSDPA) and / or High-Speed ​​UL Packet Access (HSUPA).

[0054] In one embodiment, base station 114a and WTRUs 102a, 102b, 102c can implement radio technologies such as Evolved UMTS Terrestrial Radio Access (E-UTRA), which can use Long Term Evolution (LTE) and / or Advanced LTE (LTE-A) and / or Advanced LTE Pro (LTE-A Pro) to establish air interface 116.

[0055] In one embodiment, base station 114a and WTRUs 102a, 102b, 102c can implement radio technologies such as NR radio access, which can use a new radio (NR) to establish an air interface 116.

[0056] In one embodiment, base station 114a and WTRUs 102a, 102b, and 102c can implement multiple radio access technologies. For example, base station 114a and WTRUs 102a, 102b, and 102c can jointly implement LTE radio access and NR radio access, for example, using the dual connectivity (DC) principle. Therefore, the air interface utilized by WTRUs 102a, 102b, and 102c can be characterized by multiple types of radio access technologies and / or transmissions sent to / from multiple types of base stations (e.g., eNBs and gNBs).

[0057] In other embodiments, base station 114a and WTRUs 102a, 102b, 102c can implement the following radio technologies, such as IEEE 802.11 (i.e., WiFi), IEEE 802.16 (i.e., WiMAX), CDMA2000, CDMA2000 1X, CDMA2000 EV-DO, Provisional Standard 2000 (IS-2000), Provisional Standard 95 (IS-95), Provisional Standard 856 (IS-856), Global System for Mobile Communications (GSM), Enhanced Data Rate GSM Evolution (EDGE), GSMEDGE (GERAN), etc.

[0058] Figure 1A Base station 114b can be, for example, a wireless router, a home node B, a home eNode B, or an access point, and can utilize any suitable RAT to facilitate wireless connectivity in a local area, such as a commercial area, home, vehicle, campus, industrial facility, air corridor (e.g., for drone use), road, etc. In one embodiment, base station 114b and WTRUs 102c, 102d can implement radio technologies such as IEEE 802.11 to establish a wireless local area network (WLAN). In one embodiment, base station 114b and WTRUs 102c, 102d can implement radio technologies such as IEEE 802.15 to establish a wireless personal area network (WPAN). In yet another embodiment, base station 114b and WTRUs 102c, 102d can utilize a cellular-based RAT (e.g., WCDMA, CDMA2000, GSM, LTE, LTE-A, LTE-A Pro, NR, etc.) to establish a picocell or femtocell. Figure 1A As shown, base station 114b may have a direct connection to Internet 110. Therefore, base station 114b may not be required to access Internet 110 via CN 106.

[0059] RAN 104 can communicate with CN 106, which can be any type of network configured to provide voice, data, application, and / or Voice over Internet Protocol (VoIP) services to one or more of WTRUs 102a, 102b, 102c, and 102d. Data can have different Quality of Service (QoS) requirements, such as different throughput requirements, latency requirements, error tolerance requirements, reliability requirements, data throughput requirements, mobility requirements, etc. CN 106 can provide call control, billing services, location-based services, prepaid calling, internet connectivity, video distribution, etc., and / or perform advanced security functions, such as user authentication. Although... Figure 1AAs not shown, but as will be understood, RAN 104 and / or CN 106 can communicate directly or indirectly with other RANs that use the same RAT as RAN 104 or a different RAT. For example, in addition to connecting to RAN 104, which can utilize NR radio technology, CN 106 can also communicate with another RAN (not shown) that uses GSM, UMTS, CDMA 2000, WiMAX, E-UTRA, or WiFi radio technology.

[0060] CN 106 may also act as a gateway for WTRUs 102a, 102b, 102c, and 102d to access PSTN 108, the Internet 110, and / or other networks 112. PSTN 108 may include a circuit-switched telephone network providing Common Old-Style Telephone Service (POTS). The Internet 110 may include a global system of interconnected computer networks and devices using common communication protocols such as Transmission Control Protocol (TCP), User Datagram Protocol (UDP), and / or Internet Protocol (IP) from the TCP / IP Internet Protocol suite. Network 112 may include wired and / or wireless communication networks owned and / or operated by other service providers. For example, network 112 may include another CN connected to one or more RANs, which may use the same RAT as RAN 104 or a different RAT.

[0061] Some or all of the WTRUs 102a, 102b, 102c, and 102d in communication system 100 may include multi-mode capabilities (e.g., WTRUs 102a, 102b, 102c, and 102d may include multiple transceivers for communicating with different wireless networks via different wireless links). For example, Figure 1A The WTRU 102c shown can be configured to communicate with a base station 114a that can use cellular-based radio technology and a base station 114b that can use IEEE 802 radio technology.

[0062] Figure 1B This is a system diagram illustrating example WTRU 102. (Example:) Figure 1B As shown, 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 supply 134, a global positioning system (GPS) chipset 136, and / or other peripherals 138, etc. It will be appreciated that WTRU 102 may include any sub-combination of the above-described elements while remaining consistent with the embodiments.

[0063] Processor 118 may be a general-purpose processor, a special-purpose processor, a conventional processor, a digital signal processor (DSP), multiple microprocessors, one or more microprocessors associated with a DSP core, a controller, a microcontroller, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) circuit, any other type of integrated circuit (IC), a state machine, etc. Processor 118 may perform signal encoding, data processing, power control, input / output processing, and / or any other functions that enable WTRU 102 to operate in a wireless environment. Processor 118 may be coupled to transceiver 120, which may be coupled to transmitting / receiving element 122. Although... Figure 1B The processor 118 and transceiver 120 are depicted as separate components, but it will be understood that the processor 118 and transceiver 120 can be integrated together in an electronic package or chip.

[0064] Transmitting / receiving element 122 can be configured to transmit signals to or receive signals from a base station (e.g., base station 114a) via air interface 116. For example, in one embodiment, transmitting / receiving element 122 can be an antenna configured to transmit and / or receive RF signals. In one embodiment, transmitting / receiving element 122 can be a transmitter / detector configured to transmit and / or receive, for example, IR, UV, or visible light signals. In yet another embodiment, transmitting / receiving element 122 can be configured to transmit and / or receive both RF and optical signals. It will be appreciated that transmitting / receiving element 122 can be configured to transmit and / or receive any combination of wireless signals.

[0065] Although the transmitting / receiving element 122 is in Figure 1B While depicted as a single element, WTRU 102 may include any number of transmitting / receiving elements 122. More specifically, WTRU 102 may employ MIMO technology. Thus, in one embodiment, WTRU 102 may include two or more transmitting / receiving elements 122 (e.g., multiple antennas) for transmitting and receiving wireless signals via air interface 116.

[0066] Transceiver 120 can be configured to modulate signals to be transmitted by transmitting / receiving element 122 and demodulate signals received by transmitting / receiving element 122. As noted above, WTRU 102 can have multi-mode capability. Thus, for example, transceiver 120 may include multiple transceivers for enabling WTRU 102 to communicate via multiple RATs (such as NR and IEEE 802.11).

[0067] The processor 118 of WTRU 102 can be coupled to the speaker / microphone 124, keypad 126, and / or display / touchpad 128 (e.g., a liquid crystal display (LCD) unit or an organic light-emitting diode (OLED) display unit), and can receive user input data from them. The processor 118 can also output user data to the speaker / microphone 124, keypad 126, and / or display / touchpad 128. Additionally, the processor 118 can access information from any type of suitable memory (such as non-removable memory 130 and / or removable memory 132), and store data in that memory. Non-removable memory 130 may include random access memory (RAM), read-only memory (ROM), hard disk, or any other type of memory storage device. Removable memory 132 may include a subscriber identity module (SIM) card, memory stick, secure digital storage (SD) card, etc. In other embodiments, processor 118 may access information from memory that is not physically located on WTRU 102 (such as on a server or home computer (not shown)) and store data in that memory.

[0068] The processor 118 can receive power from the power supply 134 and can be configured to distribute and / or control the power going to other components in the WTRU 102. The power supply 134 can be any suitable device for powering the WTRU 102. For example, the power supply 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, etc.

[0069] The processor 118 may also be coupled to a GPS chipset 136, which may be configured to provide location information (e.g., longitude and latitude) about the current location of the WTRU 102. In addition to, or instead of, information from the GPS chipset 136, the WTRU 102 may receive location information from base stations (e.g., base stations 114a, 114b) via air interface 116 and / or determine its location based on the timing of signals received from two or more nearby base stations. It will be understood that the WTRU 102 may acquire location information using any suitable location determination method, while remaining consistent with the embodiments.

[0070] The processor 118 may be further coupled to other peripherals 138, which may include one or more software and / or hardware modules providing additional features, functions, and / or wired or wireless connectivity. For example, peripherals 138 may include accelerometers, electronic compasses, satellite transceivers, digital cameras (for photos and / or videos), Universal Serial Bus (USB) ports, vibration devices, television transceivers, hands-free headsets, Bluetooth® modules, FM radio units, digital music players, media players, video game player modules, internet browsers, virtual reality and / or augmented reality (VR / AR) devices, activity trackers, etc. Peripherals 138 may include one or more sensors, which may be one or more of the following: gyroscopes, accelerometers, Hall effect sensors, magnetometers, orientation sensors, proximity sensors, temperature sensors, time sensors, geolocation sensors, altimeters, light sensors, touch sensors, magnetometers, barometers, gesture sensors, biometric sensors, and / or humidity sensors.

[0071] WTRU 102 may include a full-duplex radio, for which the transmission and reception of some or all signals (e.g., associated with specific subframes for both 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 hardware (e.g., a choke) or via signal processing (e.g., a separate processor (not shown) or via processor 118). In one embodiment, WTRU 102 may include a half-duplex radio, for which the transmission and reception of some or all signals (e.g., associated with specific subframes for UL (e.g., for transmission) or downlink (e.g., for reception)) may be concurrent and / or simultaneous.

[0072] Despite WTRU in Figures 1A to 1B While described as a wireless terminal, it is envisioned that, in some representative embodiments, such a terminal may use (e.g., temporarily or permanently) a wired communication interface with a communication network.

[0073] In a representative embodiment, the other network 112 may be a WLAN.

[0074] Given Figures 1A to 1B The description, along with its corresponding information, indicates that one or more of the functions described herein may be performed by one or more emulation devices (not shown). An emulation device may be one or more devices configured to emulate one or more of the functions described herein. For example, an emulation device may be used to test other devices and / or simulate network and / or WTRU functions.

[0075] Simulation devices can be designed to perform tests on one or more other devices in a laboratory environment and / or a carrier network environment. For example, the one or more simulation devices can perform one or more or all of their functions while being fully or partially implemented and / or deployed as part of a wired and / or wireless communication network to test other devices within the communication network. The one or more simulation devices can perform one or more or all of their functions while being temporarily implemented / deployed as part of a wired and / or wireless communication network. Simulation devices can be directly coupled to another device for testing purposes and / or can use over-the-air wireless communication to perform tests.

[0076] The one or more emulation devices can perform one or more (including all) functions without being implemented / deployed as part of a wired and / or wireless communication network. For example, the emulation devices can be used in test scenarios in a test laboratory and / or in non-deployed (e.g., testing) wired and / or wireless communication networks to perform testing on one or more components. The one or more emulation devices can be test equipment. Direct RF coupling and / or wireless communication via RF circuitry (e.g., which may include one or more antennas) can be used by the emulation devices to transmit and / or receive data.

[0077] Example System The embodiments described herein are not limited to implementation on WTRU. Such embodiments may use, for example, Figure 1C It is implemented by other systems such as [system name missing]. Figure 1C This is a block diagram illustrating examples of systems implementing various aspects and embodiments. System 1000 may be embodied as a device including the various components described below and 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 may be embodied individually or in combination in a single integrated circuit (IC), multiple ICs, and / or discrete components. For example, in at least one embodiment, the processing and encoder / decoder elements of system 1000 are distributed across multiple ICs and / or discrete components. In various embodiments, system 1000 is communicatively coupled to one or more other systems or other electronic devices via, for example, a communication bus or through dedicated input and / or output ports. In various embodiments, system 1000 is configured to implement one or more of the aspects described in this document.

[0078] System 1000 includes: at least one processor 1010 configured to execute instructions loaded therein for implementing various aspects, such as those described in this document. Processor 1010 may include embedded memory, input / output interfaces, and various other circuitry as known in the art. 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 may 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 memory, disk drives, and / or optical disk drives. Storage device 1040 may include internal storage devices, attached storage devices (including removable and non-removable storage devices), and / or network-accessible storage devices, as non-limiting examples.

[0079] System 1000 includes an encoder / decoder module 1030 configured to, for example, process data to provide encoded or decoded video, and the encoder / decoder module 1030 may include its own processor and memory. The encoder / decoder module 1030 represents one or more modules that may be included in a device to perform encoding and / or decoding functions. It is well known that a device may include one or both encoding and decoding modules. Alternatively, the encoder / decoder module 1030 may be implemented as a separate element of system 1000, or may be incorporated into processor 1010 as a combination of hardware and software as known to those skilled in the art.

[0080] Program code to be loaded onto processor 1010 or encoder / decoder 1030 to execute the various aspects described herein may be stored in storage device 1040 and subsequently loaded onto memory 1020 for execution by processor 1010. According to various embodiments, one or more of processor 1010, memory 1020, storage device 1040, and encoder / decoder module 1030 may store one or more items of various kinds during the execution of the processes described herein. Such stored items may include, but are not limited to, input video, decoded video or portions of decoded video, bitstreams, matrices, variables, and intermediate or final results from processing of equations, formulas, operations, and operational logic.

[0081] In some embodiments, the memory within processor 1010 and / or encoder / decoder module 1030 is used to store instructions and provide working memory for processing required during encoding or decoding. However, in other embodiments, external memory (e.g., processor 1010 or encoder / decoder module 1030) is used for one or more of these functions. External memory may be memory 1020 and / or storage device 1040, such as volatile memory and / or non-volatile flash memory. In several embodiments, external non-volatile flash memory is used to store, for example, the operating system of a television. In at least one embodiment, a fast external dynamic volatile memory, such as RAM, is used as working memory for video encoding and decoding operations, such as for MPEG-2 (MPEG stands for Moving Picture Experts Group, MPEG-2 is also known as ISO / IEC 13818, and 13818-1 is also known as H.222, and 13818-2 is also known as H.262), HEVC (HEVC stands for High Efficiency Video Coding, also known as H.265 and MPEG-H Part 2), or VVC (Common Video Coding: a new standard developed by the Joint Video Experts Team JVET)).

[0082] Inputs to the components of the system 1000 can be provided through various input devices as indicated in box 1130. Such input devices include, but are not limited to: (i) a radio frequency (RF) section that receives RF signals transmitted over the air, for example, by a broadcaster; (ii) component (COMP) input terminals (or a collection of COMP input terminals); (iii) a universal serial bus (USB) input terminal; and / or (iv) a high-definition multimedia interface (HDMI) input terminal. Figure 1C Other examples not shown include composite video.

[0083] In various embodiments, the input device of block 1130 has associated corresponding input processing elements as known in the art. For example, the RF section may be associated with elements suitable for: (i) selecting a desired frequency (also referred to as selecting a signal or limiting a signal to a frequency band); (ii) down-converting the selected signal; (iii) further limiting the frequency band to a narrower band to select, for example, a signal band that may be referred to as a channel in some embodiments; (iv) demodulating the down-converted and band-limited signal; (v) performing error correction; and (vi) demultiplexing to select the desired stream of data packets. The RF section in various embodiments includes one or more elements to perform these functions, such as frequency selectors, signal selectors, band limiters, channel selectors, filters, downconverters, demodulators, error correctors, and demultiplexers. The RF section may include a tuner that performs various of these functions, including, for example, down-converting a received signal to a lower frequency (e.g., intermediate frequency or near-baseband frequency) or to baseband. In one set-top box embodiment, the RF section and its associated input processing elements receive RF signals transmitted over a wired (e.g., cable) medium and perform frequency selection by filtering, down-converting, and filtering again to the desired frequency band. Various embodiments rearrange the order of the components described above (and others), remove some of these components, and / or add other components that perform similar or different functions. Adding components may include inserting components between existing components, such as, for example, inserting amplifiers and analog-to-digital converters. In various embodiments, the RF section includes an antenna.

[0084] Additionally, the USB and / or HDMI endpoints may include corresponding interface processors for connecting the system 1000 to other electronic devices across USB and / or HDMI connections. It should be understood that various aspects of input processing, such as Reed-Solomon error correction, can be implemented, for example, within a separate input processing IC or, if necessary, within the processor 1010. Similarly, aspects of USB or HDMI interface processing can be implemented, either within a separate interface IC or, if necessary, within the processor 1010. The demodulated, error-corrected, and demultiplexed stream is provided to various processing elements, including, for example, the processor 1010 and encoder / decoder 1030, which operate in conjunction with memory and storage elements to process the data stream as needed for presentation on an output device.

[0085] Various components of the system 1000 can be provided within an integrated housing, in which the various components can be interconnected and data can be transmitted therebetween using a suitable connection arrangement 1140, such as an internal bus as known in the art, including inter-IC (I2C) bus, wiring and printed circuit board.

[0086] System 1000 includes a communication interface 1050, which enables communication with other devices via a communication channel 1060. The communication interface 1050 may include, but is not limited to, a transceiver configured to transmit and receive data on the communication channel 1060. The communication interface 1050 may include, but is not limited to, a modem or network interface card (NIC), and the communication channel 1060 may be implemented over, for example, wired and / or wireless media.

[0087] In various embodiments, data is streamed or otherwise provided to system 1000 using a wireless network such as WiFi (e.g., IEEE 802.11 (IEEE refers to the Institute of Electrical and Electronics Engineers)). In these embodiments, the Wi-Fi signal is received on a communication channel 1060 and a communication interface 1050 adapted for Wi-Fi communication. The communication channel 1060 in these embodiments is typically connected to an access point or router that provides access to external networks, including the Internet, to allow streaming applications and other over-the-top communications. Other embodiments use a set-top box that delivers data over an HDMI connection in input box 1130 to provide streaming data to system 1000. Still other embodiments use an RF connection in input box 1130 to provide streaming data to system 1000. As indicated above, various embodiments provide data in a non-streaming manner. Additionally, various embodiments use wireless networks other than Wi-Fi, such as cellular networks or Bluetooth networks.

[0088] System 1000 can provide output signals to various output devices, including display 1100, speaker 1110, and other peripheral devices 1120. Display 1100 in various embodiments includes one or more of the following: for example, a touchscreen display, an organic light-emitting diode (OLED) display, a curved display, and / or a foldable display. Display 1100 can be used in televisions, tablets, laptops, cellular phones (mobile phones), or other devices. Display 1100 can also be integrated with other components (e.g., as in smartphones) or separate (e.g., an external monitor for a laptop). In various examples of embodiments, other peripheral devices 1120 include one or more of a standalone digital video disc (or digital multifunction disc) (DVR, for both terms), a disc player, a stereo system, and / or a lighting system. Various embodiments use one or more peripheral devices 1120 that provide functionality based on the output of system 1000. For example, a disc player performs the function of playing the output of system 1000.

[0089] In various embodiments, signaling such as AV is used to transmit control signals between system 1000 and display 1100, speaker 1110, or other peripheral devices 1120. Device-to-device control links, consumer electronics control (CEC), or other communication protocols are implemented with or without user intervention. Output devices can be communicatively coupled to system 1000 via dedicated connections through corresponding interfaces 1070, 1080, and 1090. Alternatively, output devices can be connected to system 1000 via communication interface 1050 using communication channel 1060. Display 1100 and speaker 1110 can be integrated into a single unit with other components of system 1000 in electronic devices such as, for example, televisions. In various embodiments, display interface 1070 includes a display driver, such as, for example, a timing controller (TCon) chip.

[0090] Display 1100 and speaker 1110 can alternatively be separated from one or more other components, for example, if the RF section of input 1130 is part of a separate set-top box. In various embodiments where display 1100 and speaker 1110 are external components, output signals can be provided via dedicated output connections, including, for example, HDMI ports, USB ports, or COMP outputs.

[0091] The embodiments may be implemented by computer software implemented by processor 1010, or by hardware, or by a combination of hardware and software. As a non-limiting example, the embodiments may be implemented by one or more integrated circuits. As a non-limiting example, memory 1020 may be of any type suitable for the technical environment and may be implemented using any suitable data storage technology, such as optical memory devices, magnetic memory devices, semiconductor-based memory devices, fixed memory, and removable memory. As a non-limiting example, processor 1010 may be of any type suitable for the technical environment and may encompass one or more of microprocessors, general-purpose computers, special-purpose computers, and processors based on multi-core architectures.

[0092] Detailed description Dynamic point cloud coding Example embodiments provide systems and methods for integrating motion estimation and compensation into a global point cloud compression scheme that includes both geometric structure and attributes for temporal prediction.

[0093] Motion estimation is a processing block in a compression scheme for point cloud attributes with inter-frame prediction. Motion estimation takes two consecutive point cloud frames. PC t-1 (Reference frame) and PC t (The current frame) is used as input, and a 3D motion vector field is output. It describes the time interval [ t -1, t Reference point cloud frames were created during the period. PC t-1 In order to generate the current point cloud frame PC t The predicted displacement of the point. The embodiments described herein are not limited to the use of any particular motion estimation algorithm.

[0094] The example embodiments described herein integrate motion estimation processing blocks within a compression scheme for point cloud attributes with inter-frame prediction. This disclosure describes one or more stages of the encoding / decoding process in which motion estimation is performed, and further describes examples of which input to use and which cost function to minimize.

[0095] Generally, point cloud frames PC Including geometric components G It can be described as: Includes generating point cloud frames and attribute components. A The position of the point, attribute components A It can be described as: Attribute components represent the attributes of the points, such as their color. The number of points... N It can change from frame to frame. A 3D motion vector field is derived from it. motion information M It can be represented by various models that utilize simple translations of each block with voxel accuracy to complex non-rigid deformations with sub-voxel accuracy of each arbitrary-shaped cluster.

[0096] Figure 2 An example of a point cloud encoding scheme according to some embodiments is illustrated.

[0097] In the example embodiment, the geometry and properties are compressed sequentially, such as Figure 2 The diagram illustrates this process. First, the geometry is compressed, and then the input attributes are compressed and transferred (after decompression) onto the reconstructed geometry. Thus, at the decoder side, the reconstructed geometry is available for decoding the attributes. This disclosure describes systems and methods for attribute compression that can be used in conjunction with various techniques for geometry compression.

[0098] Inter-frame motion estimation is used to provide an accurate temporal estimate of the current point cloud frame to be encoded, for both its geometric components and its properties.

[0099] One approach for motion estimation is to try to minimize the geometric prediction error. ,in It is the current point cloud frame, and It is the prediction of the point position in the current point cloud frame obtained by translating the points in the previous frame along the motion vector. Since motion information must be transmitted to the decoder, the cost function to be minimized by the motion estimation algorithm can be the classical rate-distortion optimization criterion. ,in It is an estimate of the bit rate of motion information.

[0100] This approach is designed to improve geometric prediction while reducing the transmission cost of motion vectors, but it does not guarantee that the resulting motion field is also a good predictor of point attributes. When the motion field does not correspond to "real" motion, it is highly unlikely to provide a good prediction of color attributes (unless the point color happens to be uniform).

[0101] Another approach for motion estimation is to use the prediction errors of geometry and properties (e.g., The distortion function is combined, where This represents the point attributes of the current point cloud frame, and This method predicts the point attributes of the current point cloud frame by translating points from the previous frame along a motion vector. This approach provides better attribute predictions, but at the cost of worse encoding of the geometry, and it may be difficult to find the optimal trade-off through trial and error (β).

[0102] In another approach to motion estimation, two 3D motion fields are estimated: one for predicting geometry and one for predicting attributes. Both motion fields are then transmitted to the decoder. This can be expected to provide good predictions of both geometry and attributes, but at the cost of transmitting a much larger amount of motion data.

[0103] The example embodiments disclosed herein operate to provide good motion-compensated prediction of point cloud attributes without degrading geometric coding performance or inflating the transmission cost of motion vectors within a compression scheme for dynamic point clouds.

[0104] Within the framework of a compression scheme for dynamic point clouds with motion-compensated inter-frame prediction, the example embodiment performs an improvement process for predicting motion information for point cloud attributes. The improvement process can be performed as follows: • The 3D motion transformation for predicting geometry initializes the estimate of the second motion transformation used to predict attributes. • Motion correction is searched to minimize attribute prediction error. In some embodiments, motion correction is represented by a translation model. In other embodiments, correction is represented by a rigid transformation having a combination of translation and rotation. Motion corrections are transmitted to the decoder and synthesized with the decoded geometry-based motion transformations to predict properties.

[0105] Motion of the geometric structure as a reference for property motion Figure 3 This is a functional block diagram of a point cloud attribute encoder according to some embodiments. Figure 3 The embodiments depicted are built on top of classic compression schemes for point clouds (geometry and attributes) with inter-frame prediction, such as the classic compression scheme described in "Test model for geometry-based solid point cloud – GeSTM 1.0," ISO / IEC JTC1 / SC29 / WG7, 141st MPEG meeting, Online, Tech. Rep. N00558, January 2023. Figure 4 Examples of embodiments of the corresponding decoder are described in the text. Regarding... Figure 3 The processing blocks focused on in this disclosure are blocks 302 "Motion Improvement (Attribute-Based)", 304 "Motion Coding", and 306 "Arithmetic Coding". Regarding Figure 4 The processing blocks focused in this disclosure are shaded blocks 402 "Arithmetic Decoding", 404 "Motion Decoder" and 406 "Motion Synthesis".

[0106] On the encoder side, Figure 3 In the example, the motion information estimated to predict the geometry of the point cloud is improved at 302 before being used for temporal prediction of the attributes. The motion improvement is encoded (e.g., using arithmetic coding) at 304 and 306, and the motion improvement is multiplexed with the attribute bitstream. On the decoder side, at... Figure 4 In the example, the motion improvement component is decoded at 402 and 404, and at 406 the motion improvement component is synthesized with motion information from the geometry decoder for the attribute decoder to use for motion compensation time prediction of the attribute.

[0107] The embodiments of this disclosure are not limited to any particular motion estimation algorithm or any particular motion information representation (motion model), as long as a 3D motion vector field consisting of 3D displacement vectors assigned to each point of the reference point cloud frame can be derived from the model. The motion model used with the example embodiments can be represented as follows: • The reference point cloud frame is divided into N Each segment S k , k ∈[1, N ], where each segment N kA point. A segment is a cluster that is delimited by non-overlapping 3D blocks or corresponds to an arbitrary shape. • Parametric 3D motion transformation Associated with each segment, its points p i , i ∈[1, N k Projected to position M k ( p i The predicted position is located at the current frame. The predicted position can have voxel accuracy or sub-voxel accuracy. An example of this motion transformation is achieved by a 3D translation component. and 3D rotation components A rigid transformation. Another example consists only of translation components. Therefore, the motion transformation associated with the entire point cloud frame is the union of the motion transformations of all segments: .

[0108] Motion transformation estimated between the previously decoded (reference) and current input point cloud frames based solely on the criterion of minimizing geometric prediction error. The motion transform is used by the geometry encoder to provide a temporal prediction of the current geometry. In some embodiments of this disclosure, the motion transform is used to provide a motion transform for predicting properties. Initialization. In some embodiments, The results are obtained by minimizing the prediction error based on the attribute-only prediction criterion. Correction To estimate, so that: , where “.” indicates composition.

[0109] Figure 5 A schematic diagram of motion compensation for point cloud segments from a reference frame is provided.

[0110] In some embodiments, obtaining and movement Associated attribute-based prediction error , as a point in the decoded reference frame Attributes Corresponding points of them in the current frame Attributes The average difference between them. The corresponding point can be determined as the closest to the predicted position based on the motion transformation. In one example, the attribute-based prediction error could be... Figure 5 The technique illustrated in the diagram is obtained through calculations represented by equations (1) and (2). (1) in (2).

[0111] Different embodiments may use different techniques to achieve correction. In some embodiments, exhaustive search is used to perform translation improvements. In other embodiments, iterative nearest-point techniques are used to perform rigid rotation and translation improvements. Additional improvement techniques may be used alternatively.

[0112] In some embodiments, translation improvement is performed through an exhaustive search. In this embodiment, for each segment, the translation improvement is evaluated in a small search window. The small search window can be sized The window. The translation increment that minimizes (or substantially minimizes) the attribute-based prediction error is selected. The process can be described by equation (3) and by Figure 6 To depict schematically. (3).

[0113] In some embodiments, the Iterative Closest Point (ICP) technique is used to perform improvements using rigid rotations and translations. In this embodiment, for each segment, a combination of rotation and translation improvements is determined by running the Iterative Closest Point technique. The iterative nearest point technique is modified to account for attribute prediction errors. This process is described by equation (4) and in… Figure 7 The diagram is shown schematically.

[0114] In this method, for a segment of the reference frame S k Each point in The predicted position is calculated using the motion field of the geometric structure. For each of those points From the current frame Select the corresponding matching point from the points in the middle. , where the matching point From Distance position spatial radius T The distance between the points inside the point is the same as the point outside the point inside the point. The point closest to the attribute in terms of distance. (4).

[0115] This gets the point right The set of matching points. These matching point pairs can be called nearest attribute neighbors, such as... Figure 7 The diagram in the image shows the improvement in exercise. Determined to make combined sports fields Basically minimized the position Matching points The mean square distance between positions. Improved movement. It can be an improvement in rigid motion that includes both translation and rotation.

[0116] Figure 7 The illustration shows an example where the neighbors of two matching pairs are changed from geometrically to attribute distance, enabling the estimation of the rotational increment of the segment motion using better predictions of the attributes.

[0117] In this disclosure, it is conceivable that this embodiment, or any other embodiment described herein, can be modified to allow approximations of the features disclosed herein, whether for reducing computational complexity or for other reasons. For example, replacing from radius T Inner selection matching point It can be derived from the neighborhood of a cube shape (e.g., with Center size Such points are selected within a window. Instead of minimizing the mean square error as in some embodiments, the sum of absolute differences can be minimized in other embodiments. Furthermore, where the feature is described as determining a minimum (or maximum) value, this should be understood to include an algorithm configured to minimize (or maximize) the value within actual constraints (such as within a specific number of operations or a specific search window), since determining the true global minimum (or maximum) value may be computationally expensive.

[0118] Considering the attribute-based motion improvement that can be determined using the techniques described above or other techniques. Through The direction of the previously decoded point cloud frame Perform displacement to generate the current point cloud used by the attribute encoder. Prediction of motion compensation time.

[0119] In some embodiments, to provide backward compatibility with bitstreams that do not have attribute-based motion enhancement, signaling is provided to the decoder to indicate whether the attribute bitstream contains motion enhancement components. Additionally, an encoder capable of performing such motion enhancement can decide not to perform it on a frame or frame slice basis if it is not necessary.

[0120] For example, the same embodiment modifies the G-PCC bitstream syntax by providing binary tags to the Attribute Data Unit (ADU) header of each slice of the point cloud for this purpose.

[0121] In an example embodiment, motion transformations are used for geometric prediction. It can be encoded, multiplexed with the encoded geometry into a geometric bitstream, and transmitted to the decoder using conventional techniques. Therefore, motion transformations are available when decoding attributes. Thus, the example embodiment further provides motion improvement increments. The encoding and the improvement of multiplexing the encoded attributes into the attribute bitstream.

[0122] In some embodiments, for the translational components of motion improvement, the encoding method specified in HEVC for encoding the 2D motion vector difference using motion prediction can be extended to... 3D vector updates are encoded. (Except for marker signaling) In addition, for each motion component Context updates can also be used to update two nested tags or signaling. and Entropy coding is performed. Values ​​greater than 2 can be encoded using Exp-Golomb binarization in bypass mode. Furthermore, the symbol for the motion difference can also be encoded as a marker in bypass mode.

[0123] For the rotation component, in some embodiments, the quaternion representation can be quantized and encoded. In some embodiments, only three components are transmitted. Qx , Qy and Qz This is due to the fourth component. Qw It can be inferred that: (5).

[0124] In some embodiments, the quaternion components may be encoded using a 32-bit signed integer and may be restricted to -2. 30 Up to 2 30 Within the range.

[0125] At the decoder, the motion improvement section It can be decoded from the attribute bitstream and combined with the motion information used by the geometry decoder. Synthesis. Through The direction of the previously decoded point cloud frame Perform displacement to generate the current point cloud used by the attribute decoder. Prediction of motion compensation time.

[0126] Determine the reconstructed geometry for the current point cloud frame. And from motion-compensated point cloud frames Predict the properties of points in the reconstructed geometry. This prediction can be performed using any of a variety of prediction techniques, such as... Predicting the attributes of the nearest point in the data. The attributes of points or based on Predicting by weighted (or otherwise filtered) combination of attributes of nearby points. The properties of points in the text.

[0127] Prediction of geometry as a reference for property motion As described above, some embodiments use the motion field of the geometry as a reference for predicting attribute motion. However, in other embodiments, the predicted geometry is used as a reference for predicting attribute motion. Figure 8 and 9 An example of the latter embodiment is illustrated.

[0128] Figure 8 This is a functional block diagram of a point cloud attribute encoder with attribute-based motion improvement.

[0129] In such Figure 8 In the point cloud encoding method illustrated in the figure, the current point cloud frame is composed of... This indicates that it includes geometric information. and attribute information Both. At position 802, the specific point cloud frame is determined. Geometric structure relative to the reference point cloud frame geometry-based motion information The reference point cloud frame can be a reconstructed reference frame stored in frame memory 806. At 804, based on the geometry applied to the reference point cloud frame... and attributes Both of their geometry-based motion information To generate the first motion compensation prediction for the current point cloud frame. At position 808, for the current point cloud frame... Compared to the first motion compensation prediction Determining attribute-based motion information At point 810, based on attribute-based motion information applied to the first motion compensation prediction... This is used to predict the attributes of the current point cloud frame.

[0130] In some embodiments, predicting the attributes of the current point cloud frame is performed by: using attribute-based motion information Applied to first motion compensation prediction The process involves generating a second motion compensation prediction for the current point cloud frame; determining the reconstructed geometry of the current point cloud frame; and predicting the attributes of the current point cloud frame based on the reconstructed geometry and the second motion compensation prediction.

[0131] Some embodiments further include generating a reconstructed geometry of the current point cloud frame based on a first motion-compensated predicted geometry of the current point cloud frame.

[0132] In some embodiments, attribute-based motion information includes translation information but excludes rotation information. In other embodiments, attribute-based motion information includes both translation and rotation information.

[0133] In some embodiments, attribute-based motion information is determined by an exhaustive search within a search window. In other embodiments, attribute-based motion information is determined using an iterative nearest-neighbor technique.

[0134] Figure 9 This is a functional block diagram of a point cloud attribute decoder with attribute-based motion improvement. In, for example... Figure 9 In the point cloud decoding method illustrated in the figure, geometry-based motion information is obtained by motion decoder 902 from the geometry bitstream for the current point cloud frame. At 904, a first motion compensation prediction for the current point cloud frame is generated based on the geometry-based motion information applied to the reference point cloud frame. At 906, attribute-based motion information for the current point cloud frame is obtained from the attribute bitstream. At position 908, the attributes of the current point cloud frame are predicted based on the attribute-based motion information applied to the first motion compensation prediction.

[0135] In some embodiments, the prediction of the attributes of the current point cloud frame is performed by: generating a second motion compensation prediction of the current point cloud frame by applying attribute-based motion information to a first motion compensation prediction; determining the reconstructed geometry of the current point cloud frame; and predicting the attributes of the current point cloud frame based on the reconstructed geometry and the second motion compensation prediction.

[0136] Some embodiments include: a geometry predicted by first motion compensation based on the current point cloud frame. To generate the reconstructed geometry of the current point cloud frame. .

[0137] In some embodiments, attribute-based motion information includes translation information but excludes rotation information. In other embodiments, attribute-based motion information includes both translation and rotation information.

[0138] In these examples, the motion improvement processing block has already utilized the initial motion transformation calculated based on the geometry. Motion-compensated point cloud time prediction is used as input. Geometric components have already been calculated within the geometry encoder. And utilize the displacement property components Complete the output. The motion compensation predictions used for attribute encoding at both the encoder and decoder also take this first motion-compensated point cloud as input and use motion-only improvement... To compensate for it, rather than having to compare it with synthesis.

[0139] The remainder of the improvement process is performed using the techniques described above, utilizing initial time predictions. Replace the previously decoded point cloud And utilize zero-motion substitution .

[0140] Whether the motion of the geometry or the predicted geometry is used as a reference, the example embodiments provide motion-compensated inter-frame prediction of point cloud attributes that can be performed with higher accuracy, thereby reducing the amplitude of the attribute residuals to be encoded and thus reducing the bit rate of the attribute bitstream. This may be achieved at the cost of an increased motion data bit rate (for transmitting motion field improvements). However, this improvement in bit rate may be quite limited due to the constrained search range.

[0141] Additional Examples A point cloud decoding method according to some embodiments includes: obtaining geometry-based motion information for the current point cloud frame. Obtain attribute-based motion information for the current point cloud frame. ; and based on the reference point cloud frame Synthesis of geometry-based motion information and attribute-based motion information This is used to predict the attributes of the current point cloud frame.

[0142] In some such embodiments, predicting the properties of the current point cloud frame includes: by analyzing the reference point cloud frame. The synthesis of the geometry-based motion information and the attribute-based motion information is applied. To generate motion-compensated point cloud frames. Generate the reconstructed geometry of the current point cloud frame. ; and the reconstructed geometry based on the current point cloud frame. Point cloud frames with motion compensation This is used to predict the attributes of the current point cloud frame.

[0143] Some embodiments further include: using the geometry-based motion information Geometric structure applied to the reference point cloud frame To generate inter-frame predictions of the geometry of the current point cloud frame. ; and based on the inter-frame prediction To determine the reconstructed geometry of the current point cloud frame. .

[0144] Some embodiments further include: using the geometry-based motion information Geometric structure applied to the reference point cloud frame To generate inter-frame predictions of the geometry of the current point cloud frame. The reconstructed geometry of the current point cloud frame; Based on the inter-frame prediction .

[0145] In some embodiments, the attribute-based motion information It consists of translation information but not rotation information.

[0146] In some embodiments, the attribute-based motion information This includes translation and rotation information.

[0147] A point cloud decoding method according to some embodiments includes: obtaining geometry-based motion information for the current point cloud frame. Based on the reference point cloud frame Based on geometric motion information, a first motion compensation prediction for the current point cloud frame is generated. Obtain attribute-based motion information for the current point cloud frame. ; and based on the motion compensation prediction applied to the first motion compensation prediction Attribute-based motion information This is used to predict the attributes of the current point cloud frame.

[0148] According to some embodiments, predicting the attributes of the current point cloud frame includes: predicting the attributes of the first motion compensation frame. Applying the attribute-based motion information To generate the second motion compensation prediction for the current point cloud frame. Determine the reconstructed geometry of the current point cloud frame. ; and the reconstructed geometry based on the current point cloud frame. and the second motion compensation prediction This is used to predict the attributes of the current point cloud frame.

[0149] Some embodiments further include: a geometry predicted by a first motion compensation based on the current point cloud frame. To generate the reconstructed geometry of the current point cloud frame. .

[0150] A point cloud encoding method according to some embodiments includes: determining geometry-based motion information for a current point cloud frame. Determine attribute-based motion information for the current point cloud frame. ; and based on the reference point cloud frame Synthesis of geometry-based motion information and attribute-based motion information This is used to predict the attributes of the current point cloud frame.

[0151] In some embodiments, predicting the properties of the current point cloud frame includes: by analyzing the reference point cloud frame. The synthesis of the geometry-based motion information and the attribute-based motion information is applied. To generate motion-compensated point cloud frames. Generate the reconstructed geometry of the current point cloud frame. ; and the reconstructed geometry based on the current point cloud frame. Point cloud frames with motion compensation This is used to predict the attributes of the current point cloud frame.

[0152] Some embodiments further include: using the geometry-based motion information Geometric structure applied to the reference point cloud frame To generate inter-frame predictions of the geometry of the current point cloud frame. ; and based on the inter-frame prediction To determine the reconstructed geometry of the current point cloud frame. .

[0153] Some embodiments further include: using the geometry-based motion information Geometric structure applied to the reference point cloud frame To generate inter-frame predictions of the geometry of the current point cloud frame. The reconstructed geometry of the current point cloud frame; Based on the inter-frame prediction .

[0154] In some embodiments, the attribute-based motion information Determined by exhaustive search within the search window.

[0155] In some embodiments, the attribute-based motion information It was determined using the iterative nearest point technique.

[0156] A point cloud encoding method according to some embodiments includes: determining geometry-based motion information for a current point cloud frame. Based on the reference point cloud frame Based on geometric motion information, a first motion compensation prediction for the current point cloud frame is generated. Determine attribute-based motion information for the current point cloud frame. ; and based on the motion compensation prediction applied to the first motion compensation prediction Attribute-based motion information This is used to predict the attributes of the current point cloud frame.

[0157] In some embodiments, predicting the properties of the current point cloud frame includes: predicting the properties of the first motion compensation frame. Applying the attribute-based motion information To generate the second motion compensation prediction for the current point cloud frame. Determine the reconstructed geometry of the current point cloud frame. ; and the reconstructed geometry based on the current point cloud frame. and the second motion compensation prediction This is used to predict the attributes of the current point cloud frame.

[0158] Some embodiments further include: a geometry predicted by a first motion compensation based on the current point cloud frame. To generate the reconstructed geometry of the current point cloud frame. .

[0159] A point cloud decoding apparatus according to some embodiments includes one or more processors configured to perform at least the following: obtaining geometry-based motion information for a current point cloud frame. Obtain attribute-based motion information for the current point cloud frame. ; and based on the reference point cloud frame Synthesis of geometry-based motion information and attribute-based motion information This is used to predict the attributes of the current point cloud frame.

[0160] In some embodiments, predicting the properties of the current point cloud frame includes: by analyzing the reference point cloud frame. The synthesis of the geometry-based motion information and the attribute-based motion information is applied. To generate motion-compensated point cloud frames. Generate the reconstructed geometry of the current point cloud frame. ; and the reconstructed geometry based on the current point cloud frame. Point cloud frames with motion compensation This is used to predict the attributes of the current point cloud frame.

[0161] Some embodiments further include: using the geometry-based motion information Geometric structure applied to the reference point cloud frame To generate inter-frame predictions of the geometry of the current point cloud frame. ; and based on the inter-frame prediction To determine the reconstructed geometry of the current point cloud frame. .

[0162] Some embodiments further include: using the geometry-based motion information Geometric structure applied to the reference point cloud frame To generate inter-frame predictions of the geometry of the current point cloud frame. The reconstructed geometry of the current point cloud frame; Based on the inter-frame prediction .

[0163] A point cloud decoding apparatus according to some embodiments includes one or more processors configured to perform at least the following: obtaining geometry-based motion information for a current point cloud frame. Based on the reference point cloud frame Based on geometric motion information, a first motion compensation prediction for the current point cloud frame is generated. Obtain attribute-based motion information for the current point cloud frame. ; and based on the motion compensation prediction applied to the first motion compensation prediction Attribute-based motion information This is used to predict the attributes of the current point cloud frame.

[0164] According to some embodiments, predicting the attributes of the current point cloud frame includes: predicting the attributes of the first motion compensation frame. Applying the attribute-based motion information To generate the second motion compensation prediction for the current point cloud frame. Determine the reconstructed geometry of the current point cloud frame. ; and the reconstructed geometry based on the current point cloud frame. and the second motion compensation prediction This is used to predict the attributes of the current point cloud frame.

[0165] Some embodiments further include: a geometry predicted by a first motion compensation based on the current point cloud frame. To generate the reconstructed geometry of the current point cloud frame. .

[0166] A point cloud encoding apparatus according to some embodiments includes one or more processors configured to perform at least the following: determining geometry-based motion information for a current point cloud frame. Determine attribute-based motion information for the current point cloud frame. ; and based on the reference point cloud frame Synthesis of geometry-based motion information and attribute-based motion information This is used to predict the attributes of the current point cloud frame.

[0167] In some such embodiments, predicting the properties of the current point cloud frame includes: by analyzing the reference point cloud frame. The synthesis of the geometry-based motion information and the attribute-based motion information is applied. To generate motion-compensated point cloud frames. Generate the reconstructed geometry of the current point cloud frame. ; and the reconstructed geometry based on the current point cloud frame. Point cloud frames with motion compensation This is used to predict the attributes of the current point cloud frame.

[0168] Some embodiments include: using the geometry-based motion information Geometric structure applied to the reference point cloud frame To generate inter-frame predictions of the geometry of the current point cloud frame. ; and based on the inter-frame prediction To determine the reconstructed geometry of the current point cloud frame. .

[0169] Some embodiments further include: using the geometry-based motion information Geometric structure applied to the reference point cloud frame To generate inter-frame predictions of the geometry of the current point cloud frame. The reconstructed geometry of the current point cloud frame; Based on the inter-frame prediction .

[0170] A point cloud encoding apparatus according to some embodiments includes one or more processors configured to perform at least the following: determining geometry-based motion information for a current point cloud frame. Based on the reference point cloud frame Based on geometric motion information, a first motion compensation prediction for the current point cloud frame is generated. Determine attribute-based motion information for the current point cloud frame. ; and based on the motion compensation prediction applied to the first motion compensation prediction Attribute-based motion information This is used to predict the attributes of the current point cloud frame.

[0171] In some such embodiments, predicting the properties of the current point cloud frame includes: predicting the properties of the first motion-compensated frame. Applying the attribute-based motion information To generate the second motion compensation prediction for the current point cloud frame. Determine the reconstructed geometry of the current point cloud frame. ; and the reconstructed geometry based on the current point cloud frame. and the second motion compensation prediction This is used to predict the attributes of the current point cloud frame.

[0172] Some embodiments further include: a geometry predicted by a first motion compensation based on the current point cloud frame. To generate the reconstructed geometry of the current point cloud frame. .

[0173] Further embodiments include a computer-readable medium (e.g., a non-transient medium) comprising instructions for causing one or more processors to perform any of the methods described herein.

[0174] Further embodiments include a computer-readable medium (e.g., a non-transient medium) that stores 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.

[0176] Further embodiments include a computer program product including instructions that, when executed by one or more processors, cause the one or more processors to perform any of the methods described herein.

[0177] This disclosure describes a variety of aspects, including tools, features, embodiments, models, schemes, etc. Many of these aspects are described in detail and are often described in a manner that may sound limiting, at least to illustrate individual characteristics. However, this is for clarity of purpose and does not limit the application or scope of those aspects. Indeed, all the different aspects can be combined and interchanged to provide further aspects. Furthermore, this aspect can also be combined and interchanged with aspects described in earlier submissions.

[0178] The aspects described and contemplated in this disclosure can be implemented in many different forms. Although some embodiments are specifically illustrated, other embodiments are contemplated, and the discussion of particular embodiments does not limit the breadth of implementations. 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 generated or encoded bitstream. These and other aspects can be implemented as methods, apparatus, computer-readable storage media having instructions stored thereon for encoding or decoding point cloud data according to any of the described methods, and / or computer-readable storage media having bitstreams generated according to any of the described methods stored thereon.

[0179] This document describes various methods, and each method includes one or more steps or actions for implementing the described method. Unless a specific order of steps or actions is required for the 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 elements, components, steps, operations, etc., such as, for example, "first decoding" and "second decoding." The use of such terms does not imply an ordering of the modified operations unless specifically required. Thus, in this example, the first decoding need not be performed before the second decoding, but may occur, for example, before, during, or in the time period overlapping with the second decoding.

[0180] For example, various numerical values ​​may be used in this disclosure. Specific values ​​are for illustrative purposes, and the aspects described are not limited to these specific values.

[0181] The embodiments described herein can be implemented by computer software implemented by a processor or other 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. As a non-limiting example, the processor can be of any type suitable for the technical environment and can encompass one or more of microprocessors, general-purpose computers, special-purpose computers, and processors based on multi-core architectures.

[0182] Various implementations involve decoding. As used in this disclosure, “decoding” can encompass all or part of a process performed, for example, on a received encoded sequence to produce a final output suitable for display. In various embodiments, such a process includes one or more processes typically performed by a decoder, such as entropy decoding, dequantization, inverse transform, and differential decoding. In various embodiments, such a process may also or alternatively include processes performed by a decoder of the various implementations described in this disclosure, such as: extracting an image from a block (packed) image; determining an upsampling filter to use and then upsampling the image; and flipping the image back to its intended orientation.

[0183] As a further example, in one embodiment, "decoding" refers only to entropy decoding; in another embodiment, "decoding" refers only to differential decoding; and in yet another embodiment, "decoding" refers to a combination of entropy decoding and differential decoding. Whether the phrase "decoding process" is intended to specifically refer to a subset of operations or generally to a broader decoding process will be clear based on the specific context of the description.

[0184] Various implementations involve encoding. In a manner similar to the above discussion of “decoding,” the term “encoding,” as used herein, can encompass all or part of a process performed, for example, on an input video sequence to produce an encoded bitstream. In various embodiments, such a process includes one or more processes typically performed by an encoder, such as partitioning, differential coding, transform, quantization, and entropy coding. In various embodiments, such a process also, or alternatively, includes processes performed by an encoder of the various implementations described herein.

[0185] As a further example, in one embodiment, "encoding" refers only to entropy encoding; in another embodiment, "encoding" refers only to differential encoding; and in yet another embodiment, "encoding" refers to a combination of differential and entropy encoding. Whether the phrase "encoding process" is intended to specifically refer to a subset of operations or generally to a broader encoding process will be clear based on the context of the specific description.

[0186] When a diagram is presented as a flowchart, it should be understood that it also provides a block diagram of the corresponding device. Similarly, when a diagram is presented as a block diagram, it should be understood that it also provides a flowchart of the corresponding method / process.

[0187] Various embodiments mention rate distortion optimization. Specifically, during the encoding process, a balance or trade-off between rate and distortion is typically considered, often due to computational complexity constraints. Rate distortion optimization is generally formatted as minimizing a rate distortion function, which is a weighted sum of rate and distortion. Different schemes exist for solving the rate distortion optimization problem. For example, a scheme can be based on extensive testing of all encoding options, including all considered modes or encoding parameter values, with a complete evaluation of their encoding costs and associated distortions for the reconstructed signal after encoding and decoding. Faster schemes can also be used to save encoding complexity, particularly regarding the computation of approximate distortions based on predictions or predictions of residual signals rather than the reconstructed signal. A hybrid of these two schemes can also be used, such as by using approximate distortions for only some of the possible encoding options and full distortions for others. Other schemes evaluate only a subset of the possible encoding options. More generally, many schemes employ any of a variety of techniques to perform optimization, but optimization is not necessarily a complete evaluation of both encoding costs and associated distortions.

[0188] The implementations and aspects described herein can be implemented, for example, in methods or processes, apparatus, software programs, data streams, or signals. Even if discussed only in the context of a single form of implementation (e.g., discussed only as a method), the features in question can be implemented in other forms (e.g., apparatus or program). Apparatus can be implemented, for example, in appropriate hardware, software, and firmware. Methods can be implemented, for example, in a processor, where processor generally refers to a processing device, including, for example, a computer, microprocessor, integrated circuit, or programmable logic device. Processors also include communication devices, such as, for example, computers, cellular phones, portable / personal digital assistants (“PDAs”), and other devices that facilitate communication of information between end users.

[0189] References to "an embodiment," "an embodiment," "an implementation," or "an implementation," and other variations thereof, mean that a particular feature, structure, characteristic, etc., described in connection with an embodiment is included in at least one embodiment. Therefore, the phrases "in an embodiment," "in one embodiment," "in one implementation," or "in one implementation," and any variations appearing throughout this disclosure, do not necessarily all refer to the same embodiment.

[0190] Additionally, this disclosure may refer to "determining" each piece of information. Determining information may include one or more of, for example, estimation information, calculation information, prediction information, or information retrieved from memory.

[0191] Furthermore, this disclosure may refer to "accessing" each piece of information. Accessing information may include one or more of the following: receiving information, retrieving information (e.g., from memory), storing information, moving information, copying information, calculating information, determining information, predicting information, or estimating information.

[0192] Additionally, this disclosure may refer to "receiving" individual pieces of information. As with "access," receiving is intended to be a broad term. Receiving information may include one or more of, for example, accessing information or retrieving information (e.g., from memory). Further, "receiving" typically refers to actions performed during operation, such as, for example, storing information, processing information, transmitting information, moving information, copying information, erasing information, calculating information, determining information, predicting information, or estimating information.

[0193] It should be understood that the use of any of the following “ / ”, “and / or”, and “…at least one of” (e.g., in the cases of “A / B”, “A and / or B”, and “at least one of A and B”) is intended to cover the selection of only the first listed option (A), or only the second listed option (B), or the selection of both options (A and B). As a further example, in the cases of “A, B, and / or C” and “at least one of A, B, and C”, this phrase is intended to cover the selection of only the first listed option (A), or only the second listed option (B), or only the third listed option (C), or only the first and second listed options (A and B), or only the first and third listed options (A and C), or only the second and third listed options (B and C), or the selection of all three options (A, B, and C). This can be extended to as many items as are listed.

[0194] Moreover, as used herein, the term “signaling” refers, among other things, to instructing the corresponding decoder to do something. For example, in some embodiments, the encoder signals a specific parameter among a plurality of parameters for selecting region-based filter parameters used for artifact removal filtering. In this way, in embodiments, the same parameter is used at both the encoder and decoder sides. Thus, for example, the encoder can transmit (explicitly signal) the specific parameter to the decoder so that the decoder can use the same specific parameter. Conversely, if the decoder already has the specific parameter as well as other parameters, then signaling can be used without transmission (implicitly signal) to simply allow the decoder to know and select the specific parameter. Bit saving is achieved in various embodiments by avoiding the transmission of any actual functionality. It should be understood that signaling can be done in a variety of ways. For example, in various embodiments, one or more syntax elements, tags, etc., are used to signal information to the corresponding decoder. Although the signature refers to the verb form of the term “signaling,” the term “signaling” may also be used as a noun herein.

[0195] Implementations can generate various signals formatted to carry, for example, information that can be stored or transmitted. The information may include, for example, instructions for performing a method or data generated by one of the described implementations. For example, the signal may be formatted to carry a bit stream of the described embodiments. Such a signal may be formatted as, for example, electromagnetic waves (e.g., using the radio frequency portion of the spectrum) or baseband signals. Formatting may include, for example, encoding the data stream and modulating a carrier wave using the encoded data stream. The information carried by the signal may be, for example, analog or digital information. The signal may be transmitted over a variety of different wired or wireless links, as is well known. The signal may be stored on a processor-readable medium.

[0196] We have described several embodiments. The features of these embodiments may be provided individually or in any combination across various claim classes and types.

[0197] Although features and elements are described above in specific combinations, each feature or element may be used individually or in any combination with other features and elements. Furthermore, the methods described herein can be implemented in a computer program, software, or firmware incorporated in a computer-readable medium for execution by a computer or processor. Examples of computer-readable storage media include, but are not limited to, read-only memory (ROM), random access memory (RAM), registers, cache memory, semiconductor memory devices, magnetic media (such as internal hard disks and removable disks), magnetic-optical media, and optical media (such as CD-ROMs and digital versatile discs (DVDs)). The processor associated with the software can be used to implement a radio frequency transceiver for a WTRU, UE, terminal, base station, RNC, or any host computer.

Claims

1. A point cloud decoding method, comprising: Obtain geometry-based motion information for the current point cloud frame; A first motion compensation prediction for the current point cloud frame is generated based on geometry-based motion information applied to a reference point cloud frame. Obtain attribute-based motion information for the current point cloud frame; as well as The attributes of the current point cloud frame are predicted based on the attribute-based motion information applied to the first motion compensation prediction.

2. A point cloud decoding apparatus, comprising one or more processors configured to perform at least the following: Obtain geometry-based motion information for the current point cloud frame; A first motion compensation prediction for the current point cloud frame is generated based on geometry-based motion information applied to a reference point cloud frame. Obtain attribute-based motion information for the current point cloud frame; as well as The attributes of the current point cloud frame are predicted based on the attribute-based motion information applied to the first motion compensation prediction.

3. The method of claim 1 or the apparatus of claim 2, wherein predicting the attributes of the current point cloud frame includes: The second motion compensation prediction for the current point cloud frame is generated by applying the attribute-based motion information to the first motion compensation prediction. Determine the reconstructed geometry of the current point cloud frame; as well as The attributes of the current point cloud frame are predicted based on the reconstructed geometry of the current point cloud frame and the second motion compensation prediction.

4. The method of claim 1 or claim 3 when dependent on claim 1, or the apparatus of claim 2 or claim 3 when dependent on claim 2, further comprising: Based on the geometric structure predicted by the first motion compensation of the current point cloud frame, the reconstructed geometric structure of the current point cloud frame is generated.

5. The method of claim 1 or claims 3-4 when dependent on claim 1, or the apparatus of claim 2 or claims 3-4 when dependent on claim 2, wherein the attribute-based motion information includes translation information but excludes rotation information.

6. The method of claim 1 or claims 3-4 when dependent on claim 1, or the apparatus of claim 2 or claims 3-4 when dependent on claim 2, wherein the attribute-based motion information includes translation information and rotation information.

7. A point cloud encoding method, comprising: Determine geometry-based motion information for the current point cloud frame; A first motion compensation prediction for the current point cloud frame is generated based on geometry-based motion information applied to a reference point cloud frame. Determine attribute-based motion information for the current point cloud frame; as well as The attributes of the current point cloud frame are predicted based on the attribute-based motion information applied to the first motion compensation prediction.

8. A point cloud encoding apparatus, comprising one or more processors configured to perform at least the following: Determine geometry-based motion information for the current point cloud frame; A first motion compensation prediction for the current point cloud frame is generated based on geometry-based motion information applied to a reference point cloud frame. Determine attribute-based motion information for the current point cloud frame; as well as The attributes of the current point cloud frame are predicted based on the attribute-based motion information applied to the first motion compensation prediction.

9. The method of claim 7 or the apparatus of claim 8, wherein predicting the attributes of the current point cloud frame comprises: The second motion compensation prediction for the current point cloud frame is generated by applying the attribute-based motion information to the first motion compensation prediction. Determine the reconstructed geometry of the current point cloud frame; as well as The attributes of the current point cloud frame are predicted based on the reconstructed geometry of the current point cloud frame and the second motion compensation prediction.

10. The method of claim 7 or claim 9 when dependent on claim 7, or the apparatus of claim 8 or claim 9 when dependent on claim 8, further comprising: Based on the geometric structure predicted by the first motion compensation of the current point cloud frame, the reconstructed geometric structure of the current point cloud frame is generated.

11. The method of claim 7 or, when dependent on claim 7, claims 9-10, or the apparatus of claim 8 or, when dependent on claim 8, claims 9-10, wherein the attribute-based motion information includes translation information but excludes rotation information.

12. The method of claim 7 or, when dependent on claim 7, claims 9-10, or the apparatus of claim 8 or, when dependent on claim 8, claims 9-10, wherein the attribute-based motion information includes translation information and rotation information.

13. The method of claim 7 or, when dependent on claim 7, claims 9-12, or the apparatus of claim 8 or, when dependent on claim 8, claims 9-12, wherein the attribute-based motion information is determined by an exhaustive search within a search window.

14. The method of claim 7 or, when dependent on claim 7, claims 9-12, or the apparatus of claim 8 or, when dependent on claim 8, claims 9-12, wherein the attribute-based motion information is determined using an iterative nearest-point technique.

15. A computer-readable medium comprising instructions for causing one or more processors to perform the method as claimed in claim 1 or any one of claims 3-6 when dependent on claim 1, or the method as claimed in claim 7 or any one of claims 9-14 when dependent on claim 7.

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