Patent ID: 12256096

DETAILED DESCRIPTION

Point cloud data may be generated by using, for example, a LIDAR system mounted to an automobile. The LIDAR system may emit lasers in multiple different directions in bursts over time as the automobile is moving. Thus, for a given laser emission, a point cloud may be formed. To compress the point cloud data, respective point clouds (frames) may be coded relative to each other, e.g., using intra-frame prediction or inter-frame prediction. This disclosure recognizes that because most objects around the automobile will remain relatively static, points in point clouds corresponding to the objects can be predicted using a common, global motion vector (which may be expected to generally correspond to the direction and offset traversed by the automobile). However, points along the ground may generally remain static between frames, because the lasers may be expected to identify points at the same relative positions within each frame, as the road or ground beneath the automobile is expected to be relatively flat.

Thus, this disclosure describes techniques that may reduce signaling overhead and coding information. In particular, a geometry point cloud compression (G-PCC) encoder and G-PCC decoder may be configured to separately encode and decode object and road/ground points. That is, the G-PCC encoder may be configured to classify points in a point cloud as either object points or ground/road points, then encode the object points using a global motion vector, while encoding the ground/road points separately (e.g., using zero motion vectors, a second, different global motion vector, respective local motion vectors, intra-prediction, or other distinct encoding techniques). Similarly, the G-PCC decoder may separately decode object points from road/ground points. Using a single, global motion vector for all object points in this manner may consume much fewer bits than separately using respective local motion vectors for each of the points in a point cloud. Likewise, coding all road/ground points together may reduce signaling overhead and coding data. These techniques may further reduce the number of processing operations needed to encode and decode point clouds. In this manner, these techniques may improve the operational efficiency of G-PCC encoding and decoding devices, as well as the overall field of geometry-based point cloud compression.

FIG.1is a block diagram illustrating an example encoding and decoding system100that may perform the techniques of this disclosure. The techniques of this disclosure are generally directed to coding (encoding and/or decoding) point cloud data, i.e., to support point cloud compression. In general, point cloud data includes any data for processing a point cloud. The coding may be effective in compressing and/or decompressing point cloud data.

As shown inFIG.1, system100includes a source device102and a destination device116. Source device102provides encoded point cloud data to be decoded by a destination device116. Particularly, in the example ofFIG.1, source device102provides the point cloud data to destination device116via a computer-readable medium110. Source device102and destination device116may comprise any of a wide range of devices, including desktop computers, notebook (i.e., laptop) computers, tablet computers, set-top boxes, telephone handsets such as smartphones, televisions, cameras, display devices, digital media players, video gaming consoles, video streaming devices, terrestrial or marine vehicles, spacecraft, aircraft, robots, LIDAR devices, satellites, or the like. In some cases, source device102and destination device116may be equipped for wireless communication.

In the example ofFIG.1, source device102includes a data source104, a memory106, a G-PCC encoder200, and an output interface108. Destination device116includes an input interface122, a G-PCC decoder300, a memory120, and a data consumer118. In accordance with this disclosure, G-PCC encoder200of source device102and G-PCC decoder300of destination device116may be configured to apply the techniques of this disclosure related to labeling points in a point cloud as ground or object points according to height values for the points. Thus, source device102represents an example of an encoding device, while destination device116represents an example of a decoding device. In other examples, source device102and destination device116may include other components or arrangements. For example, source device102may receive data (e.g., point cloud data) from an internal or external source. Likewise, destination device116may interface with an external data consumer, rather than include a data consumer in the same device.

System100as shown inFIG.1is merely one example. In general, other digital encoding and/or decoding devices may perform the techniques of this disclosure related to labeling points in a point cloud as ground or object points according to height values for the points. Source device102and destination device116are merely examples of such devices in which source device102generates coded data for transmission to destination device116. This disclosure refers to a “coding” device as a device that performs coding (encoding and/or decoding) of data. Thus, G-PCC encoder200and G-PCC decoder300represent examples of coding devices, in particular, an encoder and a decoder, respectively. In some examples, source device102and destination device116may operate in a substantially symmetrical manner such that each of source device102and destination device116includes encoding and decoding components. Hence, system100may support one-way or two-way transmission between source device102and destination device116, e.g., for streaming, playback, broadcasting, telephony, navigation, and other applications.

In general, data source104represents a source of data (i.e., raw, unencoded point cloud data) and may provide a sequential series of “frames”) of the data to G-PCC encoder200, which encodes data for the frames. Data source104of source device102may include a point cloud capture device, such as any of a variety of cameras or sensors, e.g., a 3D scanner or a light detection and ranging (LIDAR) device, one or more video cameras, an archive containing previously captured data, and/or a data feed interface to receive data from a data content provider. Alternatively or additionally, point cloud data may be computer-generated from scanner, camera, sensor or other data. For example, data source104may generate computer graphics-based data as the source data, or produce a combination of live data, archived data, and computer-generated data. In each case, G-PCC encoder200encodes the captured, pre-captured, or computer-generated data. G-PCC encoder200may rearrange the frames from the received order (sometimes referred to as “display order”) into a coding order for coding. G-PCC encoder200may generate one or more bitstreams including encoded data. Source device102may then output the encoded data via output interface108onto computer-readable medium110for reception and/or retrieval by, e.g., input interface122of destination device116.

Memory106of source device102and memory120of destination device116may represent general purpose memories. In some examples, memory106and memory120may store raw data, e.g., raw data from data source104and raw, decoded data from G-PCC decoder300. Additionally or alternatively, memory106and memory120may store software instructions executable by, e.g., G-PCC encoder200and G-PCC decoder300, respectively. Although memory106and memory120are shown separately from G-PCC encoder200and G-PCC decoder300in this example, it should be understood that G-PCC encoder200and G-PCC decoder300may also include internal memories for functionally similar or equivalent purposes. Furthermore, memory106and memory120may store encoded data, e.g., output from G-PCC encoder200and input to G-PCC decoder300. In some examples, portions of memory106and memory120may be allocated as one or more buffers, e.g., to store raw, decoded, and/or encoded data. For instance, memory106and memory120may store data representing a point cloud.

Computer-readable medium110may represent any type of medium or device capable of transporting the encoded data from source device102to destination device116. In one example, computer-readable medium110represents a communication medium to enable source device102to transmit encoded data directly to destination device116in real-time, e.g., via a radio frequency network or computer-based network. Output interface108may modulate a transmission signal including the encoded data, and input interface122may demodulate the received transmission signal, according to a communication standard, such as a wireless communication protocol. The communication medium may comprise any wireless or wired communication medium, such as a radio frequency (RF) spectrum or one or more physical transmission lines. The communication medium may form part of a packet-based network, such as a local area network, a wide-area network, or a global network such as the Internet. The communication medium may include routers, switches, base stations, or any other equipment that may be useful to facilitate communication from source device102to destination device116.

In some examples, source device102may output encoded data from output interface108to storage device112. Similarly, destination device116may access encoded data from storage device112via input interface122. Storage device112may include any of a variety of distributed or locally accessed data storage media such as a hard drive, Blu-ray discs, DVDs, CD-ROMs, flash memory, volatile or non-volatile memory, or any other suitable digital storage media for storing encoded data.

In some examples, source device102may output encoded data to file server114or another intermediate storage device that may store the encoded data generated by source device102. Destination device116may access stored data from file server114via streaming or download. File server114may be any type of server device capable of storing encoded data and transmitting that encoded data to the destination device116. File server114may represent a web server (e.g., for a website), a File Transfer Protocol (FTP) server, a content delivery network device, or a network attached storage (NAS) device. Destination device116may access encoded data from file server114through any standard data connection, including an Internet connection. This may include a wireless channel (e.g., a Wi-Fi connection), a wired connection (e.g., digital subscriber line (DSL), cable modem, etc.), or a combination of both that is suitable for accessing encoded data stored on file server114. File server114and input interface122may be configured to operate according to a streaming transmission protocol, a download transmission protocol, or a combination thereof.

Output interface108and input interface122may represent wireless transmitters/receivers, modems, wired networking components (e.g., Ethernet cards), wireless communication components that operate according to any of a variety of IEEE 802.11 standards, or other physical components. In examples where output interface108and input interface122comprise wireless components, output interface108and input interface122may be configured to transfer data, such as encoded data, according to a cellular communication standard, such as 4G, 4G-LTE (Long-Term Evolution), LTE Advanced, 5G, or the like. In some examples where output interface108comprises a wireless transmitter, output interface108and input interface122may be configured to transfer data, such as encoded data, according to other wireless standards, such as an IEEE 802.11 specification, an IEEE 802.15 specification (e.g., ZigBee™), a Bluetooth™ standard, or the like. In some examples, source device102and/or destination device116may include respective system-on-a-chip (SoC) devices. For example, source device102may include an SoC device to perform the functionality attributed to G-PCC encoder200and/or output interface108, and destination device116may include an SoC device to perform the functionality attributed to G-PCC decoder300and/or input interface122.

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

Input interface122of destination device116receives an encoded bitstream from computer-readable medium110(e.g., a communication medium, storage device112, file server114, or the like). The encoded bitstream may include signaling information defined by G-PCC encoder200, which is also used by G-PCC decoder300, such as syntax elements having values that describe characteristics and/or processing of coded units (e.g., slices, pictures, groups of pictures, sequences, or the like). Data consumer118uses the decoded data. For example, data consumer118may use the decoded data to determine the locations of physical objects. In some examples, data consumer118may comprise a display to present imagery based on a point cloud.

G-PCC encoder200and G-PCC decoder300each may be implemented as any of a variety of suitable encoder and/or decoder circuitry, such as one or more microprocessors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), discrete logic, software, hardware, firmware or any combinations thereof. When the techniques are implemented partially in software, a device may store instructions for the software in a suitable, non-transitory computer-readable medium and execute the instructions in hardware using one or more processors to perform the techniques of this disclosure. Each of G-PCC encoder200and G-PCC decoder300may be included in one or more encoders or decoders, either of which may be integrated as part of a combined encoder/decoder (CODEC) in a respective device. A device including G-PCC encoder200and/or G-PCC decoder300may comprise one or more integrated circuits, microprocessors, and/or other types of devices.

G-PCC encoder200and G-PCC decoder300may operate according to a coding standard, such as video point cloud compression (V-PCC) standard or a geometry point cloud compression (G-PCC) standard. This disclosure may generally refer to coding (e.g., encoding and decoding) of pictures to include the process of encoding or decoding data. An encoded bitstream generally includes a series of values for syntax elements representative of coding decisions (e.g., coding modes).

This disclosure may generally refer to “signaling” certain information, such as syntax elements. The term “signaling” may generally refer to the communication of values for syntax elements and/or other data used to decode encoded data. That is, G-PCC encoder200may signal values for syntax elements in the bitstream. In general, signaling refers to generating a value in the bitstream. As noted above, source device102may transport the bitstream to destination device116substantially in real time, or not in real time, such as might occur when storing syntax elements to storage device112for later retrieval by destination device116.

ISO/IEC MPEG (JTC 1/SC 29/WG 11) is studying the potential need for standardization of point cloud coding technology with a compression capability that significantly exceeds that of the current approaches and will target to create the standard. The group is working together on this exploration activity in a collaborative effort known as the 3-Dimensional Graphics Team (3DG) to evaluate compression technology designs proposed by their experts in this area.

Point cloud compression activities are categorized in two different approaches. The first approach is “Video point cloud compression” (V-PCC), which segments the 3D object, and project the segments in multiple 2D planes (which are represented as “patches” in the 2D frame), which are further coded by a legacy 2D video codec such as a High Efficiency Video Coding (HEVC) (ITU-T H.265) codec. The second approach is “Geometry-based point cloud compression” (G-PCC), which directly compresses 3D geometry i.e., position of a set of points in 3D space, and associated attribute values (for each point associated with the 3D geometry). G-PCC addresses the compression of point clouds in both Category 1 (static point clouds) and Category 3 (dynamically acquired point clouds). A draft of the G-PCC standard is available in G-PCC DIS, ISO/IEC JTC1/SC29/WG11 w19522, MPEG-131, Teleconference, July 2020, and a description of the codec is available in G-PCC Codec Description, ISO/IEC JTC1/SC29/WG11 w19525, MPEG-131, Teleconference, July 2020.

The units shown are logical, and do not necessarily correspond one-to-one to implemented code in the reference implementation of G-PCC codec, i.e., TMC13 test model software studied by ISO/IEC MPEG (JTC 1/SC 29/WG 11). Similarly, the units shown do not necessarily correspond one-to-one to hardware units in a hardware implementation of the G-PCC codec.

In both G-PCC encoder200and G-PCC decoder300, point cloud positions are coded first. Attribute coding depends on the decoded geometry. For Category 3 data, the compressed geometry is typically represented as an octree from the root all the way down to a leaf level of individual voxels. For Category 1 data, the compressed geometry is typically represented by a pruned octree (i.e., an octree from the root down to a leaf level of blocks larger than voxels) plus a model that approximates the surface within each leaf of the pruned octree. In this way, both Category 1 and 3 data share the octree coding mechanism, while Category 1 data may in addition approximate the voxels within each leaf with a surface model. The surface model used is a triangulation comprising 1-10 triangles per block, resulting in a triangle soup. The Category 1 geometry codec is therefore known as the Trisoup geometry codec, while the Category 3 geometry codec is known as the Octree geometry codec.

At each node of an octree, an occupancy is signaled (when not inferred) for one or more of its child nodes (up to eight nodes). Multiple neighborhoods are specified including (a) nodes that share a face with a current octree node, (b) nodes that share a face, edge or a vertex with the current octree node, etc. Within each neighborhood, the occupancy of a node and/or its children may be used to predict the occupancy of the current node or its children. For points that are sparsely populated in certain nodes of the octree, the codec also supports a direct coding mode where the 3D position of the point is encoded directly. A flag may be signaled to indicate that a direct mode is signaled. At the lowest level, the number of points associated with the octree node/leaf node may also be coded.

Once the geometry is coded, the attributes corresponding to the geometry points are coded. When there are multiple attribute points corresponding to one reconstructed/decoded geometry point, an attribute value may be derived that is representative of the reconstructed point.

There are three attribute coding methods in G-PCC: Region Adaptive Hierarchical Transform (RAHT) coding, interpolation-based hierarchical nearest-neighbour prediction (Predicting Transform), and interpolation-based hierarchical nearest-neighbour prediction with an update/lifting step (Lifting Transform). RAHT and Lifting are typically used for Category 1 data, while Predicting is typically used for Category 3 data. However, either method may be used for any data, and, just like with the geometry codecs in G-PCC, the attribute coding method used to code the point cloud is specified in the bitstream.

The coding of the attributes may be conducted in a level-of-detail (LOD), where with each level of detail, a finer representation of the point cloud attribute may be obtained. Each level of detail may be specified based on distance metric from the neighboring nodes or based on a sampling distance.

G-PCC encoder200may quantize the residuals obtained as the output of the coding methods for the attributes. G-PCC encoder200may entropy encode the quantized residuals using context adaptive arithmetic coding.

In accordance with the techniques of this disclosure, G-PCC encoder200and G-PCC decoder300may be configured to separately encode/decode points of a point cloud based on classifications of the points. In particular, G-PCC encoder200and G-PCC decoder300may be configured to classify points into, for example, ground (or road) points and object points. In some examples, a LIDAR system mounted on an automobile may project lasers into the surrounding environment to construct a point cloud. This disclosure recognizes that the ground or road on which the automobile is traveling will likely remain relatively flat and stable between frames (i.e., between respective point cloud construction instances). Thus, points collected at the position of the ground or road should be nearly identical between respective frames.

For other parts of the point cloud, identified points may correspond to non-road/ground objects. Thus, the relative positions for each of the points corresponding to non-road/ground objects may change from frame to frame in substantially the same fashion, due to the velocity of the automobile. As such, it may be efficient to encode and decode points corresponding to objects using global motion vectors and points corresponding to the road or ground using a different mechanism, e.g., a different global motion vector (such as a zero-valued global motion vector), local motion vectors, or intra-prediction.

G-PCC encoder200may determine threshold values for classifying points into either ground/road points (generally referred to as “ground” points hereinafter) or object points. For example, G-PCC encoder200may determine a top threshold and a bottom threshold, generally representing a top and bottom of the ground or road. Thus, if points are between these two thresholds, the points may be classified as ground points, and other points (e.g., points above the top threshold or below the bottom threshold) may be classified as object points. G-PCC encoder200may encode data representing the top and bottom thresholds in a data structure, such as a sequence parameter set (SPS), geometry parameter set (GPS), or geometry data unit header (GDH). G-PCC encoder200and G-PCC decoder300may therefore encode or decode occupancy of nodes above the top threshold or below the bottom threshold using a global motion vector and nodes between the top and bottom threshold using a second, different global motion vector, local motion vectors, intra-prediction, or other different prediction techniques.

In this manner, the techniques of this disclosure may result in more efficient coding of object points. Rather than coding points in the point cloud using respective local motion vectors, all of the object points between respective clouds may be predicted using a single global motion vector. Thus, signaling overhead related to signaling motion information for the object points may be drastically reduced. Moreover, because it may be largely assumed that ground points will remain constant between frames, the coding techniques for the ground points may consume a relatively low number of bits.

FIG.2is a block diagram illustrating example components of G-PCC encoder200ofFIG.1that may be configured to perform the techniques of this disclosure. In the example ofFIG.2, G-PCC encoder200includes a memory228, a coordinate transform unit202, a color transform unit204, a voxelization unit206, an attribute transfer unit208, an octree analysis unit210, a surface approximation analysis unit212, an arithmetic encoding unit214, a geometry reconstruction unit216, an RAHT unit218, a LOD generation unit220, a lifting unit222, a coefficient quantization unit224, and an arithmetic encoding unit226. InFIG.2, gray-shaded units are options typically used for Category 1 data. InFIG.2, diagonal-crosshatched units are options typically used for Category 3 data. All the other units are common between Categories 1 and 3.

As shown in the example ofFIG.2, G-PCC encoder200may receive a set of positions and a set of attributes. The positions may include coordinates of points in a point cloud. The attributes may include information about points in the point cloud, such as colors associated with points in the point cloud.

Coordinate transform unit202may apply a transform to the coordinates of the points to transform the coordinates from an initial domain to a transform domain. This disclosure may refer to the transformed coordinates as transform coordinates. Color transform unit204may apply a transform to transform color information of the attributes to a different domain. For example, color transform unit204may transform color information from an RGB color space to a YCbCr color space.

Furthermore, in the example ofFIG.2, voxelization unit206may voxelize the transform coordinates. Voxelization of the transform coordinates may include quantization and removing some points of the point cloud. In other words, multiple points of the point cloud may be subsumed within a single “voxel,” which may thereafter be treated in some respects as one point. Octree analysis unit210may also store data representing occupied voxels (i.e., voxels occupied by points of the point cloud) in memory228(e.g., in a history buffer of memory228).

Furthermore, arithmetic encoding unit214may entropy encode data representing occupancy of the octree. In some examples, arithmetic encoding unit214may entropy encode the occupancy data based only on data of a current point cloud (which may be referred to as “intra-prediction” of the current point cloud). In other examples, arithmetic encoding unit214may entropy encode the occupancy data with reference to a previous octree for a previous point cloud, e.g., buffered in memory228(which may be referred to as “inter-prediction” of the current point cloud, relative to a reference cloud). Arithmetic encoding unit214may perform inter-prediction using local or global motion vectors, e.g., as discussed below in greater detail with respect toFIG.3.

In particular, in accordance with the techniques of this disclosure, arithmetic encoding unit214may entropy decode data representing thresholds (e.g., a top threshold and a bottom threshold) for defining ground points (or road points) and object points. The top and bottom thresholds may correspond to a series of frames (point clouds). Arithmetic encoding unit214may also entropy decode data representing a global motion vector for a current point cloud of the series of frames. Arithmetic encoding unit214may form a predicted cloud using the global motion vector from a previous point cloud buffered in memory228, and use occupancy of nodes in the predicted cloud to determine context for entropy decoding occupancy data of nodes either above the top threshold or below the bottom threshold of the current cloud. Arithmetic encoding unit214may use a different prediction technique for ground/road points, such as a different global motion vector, local motion vectors, intra-prediction, or another alternative entropy decoding/prediction technique.

Additionally, in the example ofFIG.2, surface approximation analysis unit212may analyze the points to potentially determine a surface representation of sets of the points. Arithmetic encoding unit214may entropy encode syntax elements representing the information of the octree and/or surfaces determined by surface approximation analysis unit212. G-PCC encoder200may output these syntax elements in a geometry bitstream.

Geometry reconstruction unit216may reconstruct transform coordinates of points in the point cloud based on the octree, data indicating the surfaces determined by surface approximation analysis unit212, and/or other information. The number of transform coordinates reconstructed by geometry reconstruction unit216may be different from the original number of points of the point cloud because of voxelization and surface approximation. This disclosure may refer to the resulting points as reconstructed points. Attribute transfer unit208may transfer attributes of the original points of the point cloud to reconstructed points of the point cloud.

Furthermore, RAHT unit218may apply RAHT coding to the attributes of the reconstructed points. Alternatively or additionally, LOD generation unit220and lifting unit222may apply LOD processing and lifting, respectively, to the attributes of the reconstructed points. RAHT unit218and lifting unit222may generate coefficients based on the attributes. Coefficient quantization unit224may quantize the coefficients generated by RAHT unit218or lifting unit222. Arithmetic encoding unit226may apply arithmetic coding to syntax elements representing the quantized coefficients. G-PCC encoder200may output these syntax elements in an attribute bitstream.

FIG.3is a conceptual diagram illustrating an example of inter-prediction encoding in G-PCC. In some examples, G-PCC encoder200may decode/reproduce a point cloud to form reference cloud130. In other examples, G-PCC encoder200may simply store unencoded historical versions of previous point clouds. Reference cloud130may be stored in a decoded frame buffer or history buffer (i.e., a memory) of G-PCC encoder200. G-PCC encoder200may further obtain current cloud140to be encoded, at least in part, using inter-prediction. For example, G-PCC encoder200may use the techniques of this disclosure to determine a set of points of current cloud140to be predicted using global motion, as opposed to local motion or intra-prediction.

G-PCC encoder200may compare the locations of points of current cloud140to be inter-predicted to points of reference cloud130and calculate global motion vector132. Global motion vector132may represent a global motion vector that most accurately predicts locations of the points of the current cloud to be inter-predicted using global motion relative to reference cloud130. G-PCC encoder200may then form predicted cloud134by applying global motion vector132to reference cloud130. That is, G-PCC encoder200may construct predicted cloud134by applying global motion vector132to each point of reference cloud130at respective locations, and setting occupancy of nodes to include a point in predicted cloud134at a corresponding location offset by global motion vector132.

G-PCC encoder200(and in particular, arithmetic encoding unit214) may then encode points of nodes of current cloud140using corresponding points within nodes of predicted cloud134to determine contexts for context-based entropy encoding, e.g., context adaptive binary arithmetic coding (CABAC). For example, arithmetic encoding unit214may encode occupancy of current node142of current cloud140using occupancy of reference node136(which corresponds to the location of current node142as indicated by vector144) to determine context for encoding a value for the occupancy of current node142.

For example, if reference node136is occupied (that is, includes a point), arithmetic encoding unit214may determine a first context for encoding a value representing occupancy of current node142. The first context may indicate a most probable symbol for the value representing occupancy of current node142as having a high likelihood of a value representing that current node142is occupied (e.g., ‘1’). On the other hand, if reference node136is not occupied (that is, does not include any points), arithmetic encoding unit214may determine a second context for encoding the value representing occupancy of current node142. The second context may indicate a most probable symbol for the value representing occupancy of current node142as having a high likelihood of a value representing that current node142is not occupied (e.g., ‘0’). Arithmetic encoding unit142may then determine whether current node142is actually occupied, determine a value representing whether or not current node142is actually occupied, then entropy encode the value using the determined context (e.g., the first context or the second context). Arithmetic encoding unit214may add the entropy encoded value to bitstream146and proceed to a next node of current cloud140(or a next cloud).

FIG.4is a block diagram illustrating example components of G-PCC decoder300ofFIG.1that may be configured to perform the techniques of this disclosure. In the example ofFIG.4, G-PCC decoder300includes a geometry arithmetic decoding unit302, a memory324, an attribute arithmetic decoding unit304, an octree synthesis unit306, an inverse quantization unit308, a surface approximation synthesis unit310, a geometry reconstruction unit312, a RAHT unit314, a LoD generation unit316, an inverse lifting unit318, an inverse transform coordinate unit320, and an inverse transform color unit322. InFIG.4, gray-shaded units are options typically used for Category 1 data. InFIG.4, diagonal-crosshatched units are options typically used for Category 3 data. All the other units are common between Categories 1 and 3.

G-PCC decoder300may obtain a geometry bitstream and an attribute bitstream. Geometry arithmetic decoding unit302of decoder300may apply arithmetic decoding (e.g., Context-Adaptive Binary Arithmetic Coding (CABAC) or other type of arithmetic decoding) to syntax elements in the geometry bitstream. Similarly, attribute arithmetic decoding unit304may apply arithmetic decoding to syntax elements in the attribute bitstream.

Geometry arithmetic decoding unit302may entropy decode data representing occupancy of an octree for a current point cloud. In some examples, geometry arithmetic decoding unit302may entropy decode the occupancy data based only on data of a current point cloud (which may be referred to as “intra-prediction” of the current point cloud). In other examples, geometry arithmetic decoding unit302may entropy decode the occupancy data with reference to a previous octree for a previous point cloud, e.g., buffered in memory324(which may be referred to as “inter-prediction” of the current point cloud, relative to a reference cloud). Geometry arithmetic decoding unit302may perform inter-prediction using local or global motion vectors, e.g., as discussed below in greater detail with respect toFIG.5.

In particular, in accordance with the techniques of this disclosure, geometry arithmetic decoding unit302may entropy decode data representing thresholds (e.g., a top threshold and a bottom threshold) for defining ground points (or road points) and object points. The top and bottom thresholds may correspond to a series of frames (point clouds). Geometry arithmetic decoding unit302may also entropy decode data representing a global motion vector for a current point cloud of the series of frames. Geometry arithmetic decoding unit302may form a predicted cloud using the global motion vector from a previous point cloud buffered in memory324, and use occupancy of nodes in the predicted cloud to determine context for entropy decoding occupancy data of nodes either above the top threshold or below the bottom threshold of the current cloud. Geometry arithmetic decoding unit302may use a different prediction technique for ground/road points, such as a different global motion vector, local motion vectors, intra-prediction, or another alternative entropy decoding/prediction technique.

Octree synthesis unit306may synthesize an octree based on data for syntax elements parsed from the geometry bitstream and entropy decoded by geometry arithmetic decoding unit302. In instances where surface approximation is used in the geometry bitstream, surface approximation synthesis unit310may determine a surface model based on syntax elements parsed from the geometry bitstream and based on the octrec.

Furthermore, geometry reconstruction unit312may perform a reconstruction to determine coordinates of points in a point cloud. Inverse transform coordinate unit320may apply an inverse transform to the reconstructed coordinates to convert the reconstructed coordinates (positions) of the points in the point cloud from a transform domain back into an initial domain.

Additionally, in the example ofFIG.4, inverse quantization unit308may inverse quantize attribute values. The attribute values may be based on syntax elements obtained from the attribute bitstream (e.g., including syntax elements decoded by attribute arithmetic decoding unit304).

Depending on how the attribute values are encoded, RAHT unit314may perform RAHT coding to determine, based on the inverse quantized attribute values, color values for points of the point cloud. Alternatively, LOD generation unit316and inverse lifting unit318may determine color values for points of the point cloud using a level of detail-based technique.

Furthermore, in the example ofFIG.4, inverse transform color unit322may apply an inverse color transform to the color values. The inverse color transform may be an inverse of a color transform applied by color transform unit204of encoder200. For example, color transform unit204may transform color information from an RGB color space to a YCbCr color space. Accordingly, inverse color transform unit322may transform color information from the YCbCr color space to the RGB color space.

The various units ofFIG.2andFIG.4are illustrated to assist with understanding the operations performed by encoder200and decoder300. The units may be implemented as fixed-function circuits, programmable circuits, or a combination thereof. Fixed-function circuits refer to circuits that provide particular functionality, and are preset on the operations that can be performed. Programmable circuits refer to circuits that can be programmed to perform various tasks, and provide flexible functionality in the operations that can be performed. For instance, programmable circuits may execute software or firmware that cause the programmable circuits to operate in the manner defined by instructions of the software or firmware. Fixed-function circuits may execute software instructions (e.g., to receive parameters or output parameters), but the types of operations that the fixed-function circuits perform are generally immutable. In some examples, one or more of the units may be distinct circuit blocks (fixed-function or programmable), and in some examples, one or more of the units may be integrated circuits.

FIG.5is a conceptual diagram illustrating an example of inter-prediction decoding in G-PCC. In accordance with the techniques of this disclosure, G-PCC decoder300may use the global motion vector inter-prediction techniques ofFIG.5to decode a set of points of current cloud160, and local motion vector inter-prediction or intra-prediction to decode a second set of points of current cloud160. G-PCC decoder300may receive and decode data of bitstream166representing whether sets of points for one or more nodes are to be decoded using global motion vector inter-prediction.

G-PCC decoder300may initially decode one or more previous point clouds and store the previously decoded point clouds in a decoded frame buffer or history buffer (i.e., a memory of G-PCC decoder300). G-PCC decoder300may also decode motion information including data for global motion vector152and identifying reference cloud150in the previously decoded point clouds.

G-PCC decoder300may apply global motion vector152to reference cloud150to generate predicted cloud154. That is, G-PCC decoder300may construct predicted cloud154by applying global motion vector152to each point of reference cloud150at respective locations, and setting occupancy of nodes (e.g., reference node156) of predicted cloud154to include a point at a corresponding location offset by global motion vector152.

Geometry arithmetic decoding unit302may then use the occupancy of nodes of predicted cloud154(e.g., reference node156) to determine a context for decoding a value representing occupancy of current node162of current cloud160. Current cloud162corresponds to reference node156as indicated by vector164. For example, if reference node156is occupied (that is, includes a point), geometry arithmetic decoding unit302may determine a first context for encoding a value representing occupancy of current node162. The first context may indicate a most probable symbol for the value representing occupancy of current node162as having a high likelihood of a value representing that current node162is occupied (e.g., ‘1’). On the other hand, if reference node156is not occupied (that is, does not include any points), geometry arithmetic decoding unit302may determine a second context for encoding the value representing occupancy of current node162. The second context may indicate a most probable symbol for the value representing occupancy of current node162as having a high likelihood of a value representing that current node162is not occupied (e.g., ‘0’). Geometry arithmetic decoding unit302may then decode a value of bitstream166representing occupancy of current node162using the determined context.

FIG.6is a conceptual diagram illustrating an example prediction tree that may be used when performing the techniques of this disclosure. Predictive geometry coding was introduced in “Exploratory model for inter-prediction in G-PCC,” ISO/IEC JTC1/SC29 WG11, Document N18096, Macau, CN, October 2018, as an alternative to octrec geometry coding. In predictive geometry coding, nodes are arranged in a tree structure (which defines the prediction structure), and various prediction strategies are used to predict the coordinates of each node in the tree with respect to its predictors.

FIG.6illustrates an example prediction tree350, which is a directed graph where the arrows point in the prediction direction. Prediction tree350includes various types of nodes according to a number of children (e.g., 0 to 3). In the example ofFIG.6, node352is an example of a branch vertex with three children, node354is an example of a branch node with two children, node356is an example of a branch node with one child, node358represents an example of a leaf vertex, and node360represents an example of a root vertex. As the root vertex, node360has no predictors. Every node in prediction tree350has at most one parent node.

Four prediction strategies may be specified for a current node based on its parent (p0), grand-parent (p1) and great-grand-parent (p2): 1) no prediction/zero prediction (0); 2) delta prediction (p0); 3) linear prediction (2*p0−p1); parallelogram prediction (2*p0+p1−p2).

G-PCC encoder200may employ any algorithm to generate the prediction tree. G-PCC encoder200may determine the algorithm to be used according to the application/use case, and several strategies may be used. Some strategies are described in N18096.

For each node, G-PCC encoder200may encode residual coordinate values in the bitstream starting from the root node in a depth-first manner. Predictive geometry coding may be particularly useful for Category 3 (e.g., LIDAR-acquired) point cloud data, e.g., for low-latency applications.

FIG.7is a conceptual diagram illustrating an example spinning LIDAR acquisition model.FIG.7illustrates LIDAR380, which includes a number of sensors that emit and receive respective lasers382. G-PCC includes an angular mode for predictive geometry coding. In angular mode, characteristics of LIDAR sensors may be used to code the prediction tree more efficiently. In angular mode, coordinates of positions are converted to radius (r)384, azimuth (ϕ)386, and laser index (i) 388 values. G-PCC encoder200and G-PCC decoder300may perform prediction in this domain. That is, G-PCC encoder200and G-PCC decoder300may code residual values in the in r, ϕ, i domain.

Due to errors in rounding, coding in r, o, i domain is not lossless. Therefore, G-PCC encoder200and G-PCC decoder300may code a second set of residuals that correspond to Cartesian coordinates. A description of the encoding and decoding strategies used for angular mode for predictive geometry coding is reproduced below from N18096.

Angular mode for predictive geometry coding focuses on point clouds acquired using a spinning Lidar model. In the example ofFIG.7, LIDAR380has N lasers382(e.g., where N may be equal to 16, 32, 64, or some other value) spinning around the Z axis according to an azimuth angle ϕ. Each of lasers382may have a different elevation angle θ(i)i=1 . . . . Nand height ζ(i)i=1 . . . . N. Suppose that laser i hits a point M, with cartesian integer coordinates (x, y, z), defined according to a coordinate system.

According to N18096, the position of point M may be modelled with the three parameters (r, ϕ, i), which may be computed as follows:

r=x2+y2⁢ϕ=a⁢tan⁢2⁢(y,x)⁢i=argminj=1⁢…⁢N{z+ς⁡(j)-r×tan⁡(θ⁡(j))},

More precisely, G-PCC encoder200and G-PCC decoder300may use the quantized versions of (r, ϕ, i), denoted ({tilde over (r)}, {tilde over (ϕ)}, i), where the three integers {tilde over (r)}, {tilde over (ϕ)} and i may be computed as follows:

r~=floor(x2+y2qr+or)=hypot⁡(x,y)⁢ϕ~=sign⁡(a⁢tan⁢2⁢(y,x))×floor(❘"\[LeftBracketingBar]"a⁢tan⁢2⁢(y,x)❘"\[RightBracketingBar]"qϕ+oϕ)⁢i=argminj=1⁢…⁢N{z+ς⁡(j)-r×tan⁡(θ⁡(j))}where(qr, or) and (qϕ, oϕ) are quantization parameters controlling the precision of {tilde over (ϕ)} and {tilde over (r)}, respectively.sign(t) is the function that return 1 if t is positive and (−1) otherwise.|t| is the absolute value of t.

To avoid reconstruction mismatches due to the use of floating-point operations, G-PCC encoder200and G-PCC decoder300may pre-compute and quantize values of ζ(i)i=1 . . . . . Nand tan(θ(i))i=1 . . . Nas follows:

z~(i)=sign⁡(ς⁡(i))×floor(❘"\[LeftBracketingBar]"ς⁡(i)❘"\[RightBracketingBar]"qς+oς)⁢t~(i)=sign(ς⁡(tan⁡(θ⁡(j)))×floor(❘"\[LeftBracketingBar]"tan(θ⁡(j)❘"\[RightBracketingBar]"qθ+oθ)where(qζ, oζ) and (qθ, oθ) are quantization parameters controlling the precision of {tilde over (ζ)} and {tilde over (θ)}, respectively.

G-PCC encoder200and G-PCC decoder300may obtain the reconstructed cartesian coordinates as follows:

x^=round(r~×qr×app_cos⁢(ϕ~×qϕ))⁢y^=round(r~×qr×app_sin⁢(ϕ~×qϕ))⁢z^=round(r~×qr×t~(i)×qθ-z~(i)×qς),
where app_cos(.) and app_sin(.) are approximations of cos(.) and sin(.), respectively. The calculations could use a fixed-point representation, a look-up table, and/or linear interpolation.

For various reasons, such as quantization, approximations, model imprecision, and/or model parameters imprecisions, ({circumflex over (x)}, ŷ, {circumflex over (z)}) may be different from (x, y, z).

Let (rx, ry, rz) be the reconstruction residuals defined as follows:

rx=x-x^⁢ry=y-y^⁢rz=z-z^

G-PCC encoder200may proceed as follows:· Encode the model parameters {tilde over (t)}(i) and {tilde over (z)}(i) and the quantization parameters qrqζ, qθand qϕ.Apply the geometry predictive scheme described in w19522 to the representation ({tilde over (r)}, {tilde over (ϕ)}, i):A new predictor leveraging the characteristics of lidar could be introduced. For instance, the rotation speed of the lidar scanner around the z-axis is usually constant. Therefore, G-PCC encoder200may predict the current {tilde over (ϕ)}(j) as follows:

ϕ~(j)=ϕ~(j-1)+n⁡(j)×δϕ(k),where(δϕ(k))k=1 . . . Kis a set of potential speeds the encoder could choose from. G-PCC encoder200may explicitly encode index k to the bitstream, or G-PCC decoder300could infer index k from context based on a deterministic strategy applied by both G-PCC encoder200and G-PCC decoder300, andn(j) is the number of skipped points which G-PCC encoder could explicitly encode to the bitstream, or G-PCC decoder300could infer n(j) from context based on a deterministic strategy applied by both G-PCC encoder200and G-PCC decoder300. n(j) is also referred to as the “phi multiplier.” n(j) may be used with a delta predictor.Encode with each node the reconstruction residuals (rx, ry, rz)

G-PCC decoder300may proceed as follows:Decode the model parameters {tilde over (t)}(i) and {tilde over (z)}(i) and the quantization parameters qrqζ, qθand qϕ.Decode the ({tilde over (r)}, {tilde over (ϕ)}, i) parameters associated with the nodes according to the geometry predictive scheme described in w19522.Compute the reconstructed coordinates ({circumflex over (x)}, ŷ, {circumflex over (z)}) as described above.Decode the residuals (rx, ry, rz).Lossy compression could be supported by quantizing the reconstruction residuals (rx, ry, rz).Compute the original coordinates (x, y, z) as follows:

x=rx+x^⁢y=ry+y^⁢z=rz+z^

Lossy compression may be achieved if G-PCC encoder200applies quantization to the reconstruction residuals (rx, ry, rz) or drops points. Quantized reconstruction residuals may be computed as follows:

r~x=sign⁡(rx)×floor(❘"\[LeftBracketingBar]"rx❘"\[RightBracketingBar]"qx+ox)⁢r~y=sign⁡(ry)×floor(❘"\[LeftBracketingBar]"ry❘"\[RightBracketingBar]"qy+oy)⁢r~z=sign⁡(rz)×floor(❘"\[LeftBracketingBar]"rz❘"\[RightBracketingBar]"qz+oz),
where (qx, ox), (qy, oy) and (qz, oz) are quantization parameters controlling the precision of {tilde over (r)}x, {tilde over (r)}yand {tilde over (r)}z, respectively.

Trellis quantization could be used to further improve the RD (rate-distortion) performance results. The quantization parameters may change at sequence/frame/slice/block level to achieve region adaptive quality and for rate control purposes.

FIG.8is a flowchart illustrating an example motion estimation process for G-PCC InterEM software. There are two kinds of motion involved in G-PCC InterEM software, a global motion matrix and a local node motion vector. Global motion parameters are defined as a rotation matrix and translation vector, which will be applied on all the points in a prediction (reference) frame. A local node motion vector of a node of the octree is a motion vector that may only be applied on points within the node in a prediction (reference) frame. Details of the motion estimation algorithm in InterEM are described below.FIG.8illustrates flowchart for a motion estimation algorithm.

Given input prediction (reference) frame and current frame, G-PCC encoder200may first estimate global motion at a global scale (400). G-PCC encoder200may then apply estimated global motion to the prediction (reference) frame (402). After applying global motion on the prediction (reference) frame, G-PCC encoder200may estimate local motion at a finer scale (404), e.g., node level in octrec. Finally, G-PCC encoder200may perform motion compensation (406) to encode the estimated local node motion vectors and points.

FIG.9is a flowchart illustrating an example process for estimating global motion. In the InterEM software, the global motion matrix is defined to match feature points between the prediction frame (reference) and the current frame.FIG.9illustrates the pipeline for estimating global motion. The global motion estimation algorithm may be divided into three steps: finding feature points (410), sampling feature points pairs (412), and motion estimation using a Least Mean Square (LMS) algorithm (414).

The algorithm defines feature points to be those points that have large position change between the prediction frame and current frame. For each point in the current frame, G-PCC encoder200finds the closest point in the prediction frame and builds point pairs between the current frame and the prediction frame. If the distance between the paired points is greater than a threshold, G-PCC encoder200regards the paired points as feature points.

After finding the feature points, G-PCC encoder200performs a sampling on the feature points to reduce the scale of the problem (e.g., by choosing a subset of feature points to reduce the complexity of motion estimation). Then, G-PCC encoder200applies the LMS algorithm to derive motion parameters by attempting to reduce the error between respective features points in the prediction frame and the current frame.

FIG.10is a flowchart illustrating an example process for estimating a local node motion vector. G-PCC encoder200may estimate the motion vectors for nodes of the prediction tree in a recursive manner. G-PCC encoder200may evaluate a cost function for selecting a best suitable motion vector based on rate-distortion (RD) costs.

In the example ofFIG.10, G-PCC encoder200receives a current node (420). If the current node is not split into 8 children, G-PCC encoder200determines a motion vector that would result in the lowest cost between the current node and the prediction node (422). On the other hand, if the current node is divided into 8 children, G-PCC encoder200divides the current node into 8 children (424), finds motion for each of the child nodes (426), and adds all returned estimated costs (428). That is, G-PCC encoder200applies a motion estimation algorithm and obtains a total cost under a split condition by adding the estimated cost value of each child node. G-PCC encoder200may determine whether to split or not split a node by comparing costs between splitting and not splitting. If split, G-PCC encoder200may assign each sub-node its respective motion vector (or further split the node into respective child nodes). If not split, G-PCC encoder200may assign the node its motion vector. G-PCC encoder200may then compare the costs to determine whether to split the current node or not split the current node (430).

Two parameters that may affect the performance of motion vector estimation are block size (BlockSize) and minimum prediction unit size (MinPUSize). BlockSize defines the upper bound of node size to apply motion vector estimation and MinPUSize defines the lower bound.

U.S. Provisional Patent No. 63/090,657, filed Oct. 12, 2020, converted as U.S. patent application Ser. No. 17/495,428, filed Oct. 2021, described an improved global motion estimation technique based on Iterative Closest Point scheme. In this scheme, first, an initial translation vector is estimated by minimizing the mean squared error between the current frame and the reference frame. When estimating the initial translation vector, labels for whether a point is ground or not could be taken into consideration. For example, if a point is a ground point, then this point is excluded from the estimation. The initial translation vector combined with identity matrix may then be fed into the Iterative Closest Point scheme or a similar scheme to estimate the rotation matrix and the translation vector. Also, in this case, whether a point is ground or not could be taken into consideration, for example, by excluding it from the estimation. Alternatively, the rotation matrix may be estimated first, based on labels for whether a point is ground or not. The label may be derived by G-PCC encoder200and signaled to G-PCC decoder300, or G-PCC encoder200and G-PCC decoder300may derive the label. The label may be derived based on ground estimation algorithms; such algorithms could be based on the height of a point, density of the point cloud in the neighborhood of the point, relative distance of the point from the LIDAR origin/fixed points, etc.

In real applications, such as automotive, the ground area and the objects in a point cloud typically have different motions. For example, the ground points may have zero motion or small motion, while the objects may have higher motion. In the traditional method to estimate global motion in InterEM software, both ground points and object points may be used to derive the global motion. After doing so, the output of the estimation may not be accurate.

U.S. Provisional Patent No. 63/090,657, filed Oct. 12, 2020, introduced several labeling methods to classify objects and ground. For example, in these methods, G-PCC encoder200may derive the label and signal the label to G-PCC decoder300, or both G-PCC encoder200and G-PCC decoder300may derive the label. The label may be derived based on a ground estimation algorithm; such algorithms could be based on the height of a point, density of the point cloud in the neighborhood of the point, relative distance of the point from the LIDAR origin/fixed points, etc.

This disclosure describes techniques for labeling ground and objects to improve the performance of global motion estimation. In particular, G-PCC encoder200and G-PCC decoder300may be configured to classify ground/road and object data in a point cloud, which may improve the performance of global motion estimation.

FIG.11is a graph illustrating an example classification of a cloud into ground (road) and objects using two thresholds of z-values of points according to the techniques of this disclosure. G-PCC encoder200and G-PCC decoder300may be configured to classify ground and road points using the height (or z-value) of points in the cloud. In an example, G-PCC encoder200and G-PCC decoder300may be configured with definitions for two thresholds, e.g., z_top452and z_bottom454as shown inFIG.11.

If the height (z-value) of a point is lower than z_bottom454or higher than z_top452, G-PCC encoder200and G-PCC decoder300may classify the point as an object. Otherwise, if the point has a height (z-value) between z_bottom454and z_top452, G-PCC encoder200and G-PCC decoder300may classify the point as ground (road).

In some examples, G-PCC encoder200and G-PCC decoder300may specify ground points using a set of value ranges and classify the ground points as including any point that satisfies at least one of the value ranges. For example, for an (x, y, z) coordinate, G-PCC encoder200and G-PCC decoder300may be configured with a specification of an ithvalue range as {(x_mini, x_maxi), (y_mini, y_maxi), (z_mini, z_maxi)}. G-PCC encoder200and G-PCC decoder300may be configured with N such ranges, such that i is in [1, N]. G-PCC encoder200and G-PCC decoder300may classify a point at (x, y, 2) as a ground point if ((x_mini≤x≤x_maxi) & (y_mini≤y≤y_maxi) & (z_mini≤z≤z_maxi)) for some value of i in [1, N] (or alternatively, i in [0, N−1]).

G-PCC encoder200and G-PCC decoder300may be configured with min values down to negative infinity and max values up to infinity. In the example above regarding z-values for points between z_bottom454and z_top452being classified as ground and other points being classified as objects, x_miniand y_minimay be set to negative infinity, and x_maxiand y_maximay be set to infinity, while z_minimay be set to z_bottom454and z_maximay be set to z_top452.

When G-PCC encoder200is configured to quantize the point cloud before encoding by a scaling factor, G-PCC encoder200and G-PCC decoder300may also quantize the threshold values using the same quantization factor.

In addition or in the alternative, G-PCC encoder200and G-PCC decoder300may be configured to use the output of the classification of points (e.g., into road and ground points) in global motion estimation and prediction. G-PCC encoder200and G-PCC decoder300may estimate global motion according to the techniques of the InterEM software or the method described in U.S. Provisional Application No. 63/090,657 as discussed above.

Alternatively, in some examples, G-PCC encoder200and G-PCC decoder300may derive two global motion sets. G-PCC encoder200and G-PCC decoder300may use the first set of global motion information to predict ground/road points and the second set to predict object points. To derive the global motion set for ground/road, only the points with the label of “ground/road” may be used. To derive the global motion set for objects, only the points with the label “object” may be used.

As yet another example, G-PCC encoder200and G-PCC decoder300may only derive one global motion set to predict object points. In this example, G-PCC encoder200and G-PCC decoder300may predict ground/road points using zero motion (translation and rotation set equal to zero).

In some examples, which may be used in addition to the various techniques discussed above, G-PCC encoder200and G-PCC decoder300may define a threshold for different levels of sharing. For example, G-PCC encoder200and G-PCC decoder300may determine a threshold independently for different frames. G-PCC encoder200may determine thresholds of each frame and encode data representing the thresholds in the bitstream, such that G-PCC decoder300can determine the thresholds from the encoded data of the bitstream.

In some examples, G-PCC encoder200and G-PCC decoder300may define the thresholds at the level of a group of pictures (GOP). In this example, all frames in the GOP may share the same thresholds. G-PCC encoder200and G-PCC decoder300may determine the shared thresholds at the beginning of the GOP and code data jointly with encoding information of an ordinal first frame of the GOP.

In some examples, G-PCC encoder200and G-PCC decoder300may define thresholds at the sequence level. That is, all frames in the sequence may share the same thresholds. In this example, the thresholds may be presented in an encoder configuration data (e.g., an encoder configuration file) for G-PCC encoder200, and G-PCC encoder200may encode data representing the thresholds in the bitstream such that G-PCC decoder300can determine the thresholds from the encoded data.

G-PCC encoder200may derive the threshold that applies to a set (e.g., GOP, sequence, etc.) of two or more frames using various techniques:In the simplest case, G-PCC encoder200may select the threshold of the ordinal first frame in the set as the threshold for frames in the set. The ordinal first frame may be the ordinal first frame in the output order or the decoding order of the point cloud.In some examples, G-PCC encoder200may derive the threshold used according to a weighted average of thresholds derived for/applicable to two more frames in the set. For example, if there are 10 frames in the set, and t1i, t2irefers to the thresholds derived for the i-th frame, the final threshold may be derived as follows for n equal to 1 and 2:

tnf=∑i=110⁢wi⁢tniAn example of the weights may be set uniformly for all the frames in the set. The weights may also be specified such that only some of the frames are used for calculating the final threshold; e.g., every 8-th frame may be chosen to have a non-zero weight, and all other frames may be given a 0 weight. The weights may also be specified based on a temporal ID of the point cloud; frames that belong to lower temporal ID may get a larger weight, and frames that belong to higher temporal ID may get a smaller weight.In some alternatives, G-PCC encoder200may be configured with constraints on the sum of the weights used to derive a threshold being equal to 1.

In some examples, G-PCC encoder200may derive the thresholds and encode data in the bitstream such that G-PCC decoder300can determine the thresholds from the encoded data. In some examples, both G-PCC encoder200and G-PCC decoder300may derive the thresholds according to the same techniques.

In some examples, when G-PCC encoder200signals the thresholds, G-PCC encoder200may signal the thresholds in a sequence parameter set (SPS), a geometry parameter set (GPS), or in a geometry data unit header (GDH). In one example, where G-PCC encoder200signals the thresholds in the GPS/GDH level, G-PCC encoder200may be configured to conditionally signal the thresholds, e.g., only when angular mode is enabled. Thus, when angular mode is enabled, G-PCC encoder200and G-PCC decoder300may be configured to code the data for the thresholds in the GPS/GDH, whereas when angular mode is disabled, G-PCC encoder200and G-PCC decoder300may be configured to avoid coding the data from the thresholds. Alternatively, G-PCC encoder200and G-PCC decoder300may be configured to code the data for the thresholds unconditionally.

G-PCC encoder200and G-PCC decoder300may code data for the thresholds as se(v) or ue(v) values. Se(v) coding may represent a signed integer 0thorder Exp-Golomb-coded syntax element with the left bit first, while ue(v) coding may represent an unsigned integer 0thorder Exp-Golomb-coded syntax element with the left bit first. In one example, the GPS may be modified as follows in Table 1, where [added: “added text”] represents additions to the existing G-PCC standard, and according to which G-PCC encoder200and G-PCC decoder300may be configured:

TABLE 1geometry_angular_enabled_flagu(1)if( geometry_angular_enabled_flag ){[added: “geom_globmotion_threshold0se(v)”][added: “geom_globmotion_threshold1se(v)”]geom_slice_angular_origin_present_flagu(1)if( !geom_slice_angular_origin_present_flag ) {geom_angular_origin_bits_minus1ue(v)for( k = 0; k < 3; k++ )geom_angular_origin_xyz[ k ]s(v)}if( geom_tree_type == 1 ) {geom_angular_azimuth_scale_log2ue(v)geom_angular_azimuth_step_minus1ue(v)geom_angular_radius_scale_log2ue(v)}number_lasers_minus1ue(v)laser_angle_initse(v)laser_correction_initse(v)if( geom_tree_type = = 0 )laser_phi_per_turn_init_minus1ue(v)for( i = 1; i <= number_lasers_minus1; i++ ) {laser_angle_diff[ i ]se(v)laser_correction_diff[ i ]se(v)if( geom_tree_type = = 0 )laser_phi_per_turn_diff [ i ]se(v)}if( geometry_planar_enabled_flag )planar_buffer_disabled_flagu(1)}

Alternatively, if the threshold values are large enough, a fixed-length coding can also be performed, including indicating the number of bits to be coded for fixed-length coding, followed by actual fixed-length coding of thresholds using s(v) coding, e.g., per Table 2 below. S(v) coding represents signed fixed length coding of a value:

TABLE 2geometry_angular_enabled_flagu(1)if( geometry_angular_enabled_flag ){[added: “geom_globmotion_thresholds_bits_minus1ue(v)”][added: “geom_globmotion_threshold0s(v)”][added: “geom_globmotion_threshold1s(v)”]geom_slice_angular_origin_present_flagu(1)if( !geom_slice_angular_origin_present_flag ) {...}

As shown inFIG.11, there may be a maximum of two threshold values (z_bottom, z_top) in the scenario of classifying top, road, and bottom regions. In a typical scenario, the origin of the frame may be the center of LIDAR system, indicating z_top and z_bottom both are likely to be negative as the LIDAR system center/frame origin is likely to be well above the road. Secondly, the thresholds can be always arranged in a descending order, i.e., z_top>z_bottom. In such cases, G-PCC encoder200and G-PCC decoder300may be configured to code the first threshold as it is, and for the second threshold, G-PCC encoder200and G-PCC decoder300may code the difference between the second threshold value and the first threshold value. Moreover, as this delta would always be negative, it is possible to infer the sign of the delta, thus only the magnitude of the difference may be coded. Moreover, the difference cannot be zero, so instead magnitude of delta minus 1 may be coded. In certain scenarios, a single threshold may be enough when the bottom region is not very apparent. So, G-PCC encoder200and G-PCC decoder300may be configured to code a flag to indicate whether a second threshold is present or not. Syntax modification for the GPS, according to which G-PCC encoder200and G-PCC decoder300may be configured, may be as shown in Table 3 as follows:

TABLE 3geometry_angular_enabled_flagu(1)if( geometry_angular_enabled_flag ){[added: “geom_globmotion_threshold0se(v)”][added: “geom_globmotion_threshold1_presentu(1)”][added: “if(geom_globmotion_threshold1_present)”][added:ue(v)”]“geom_globmotion_threshold1_absdelta_minus1geom_slice_angular_origin_present_flagu(1)if( !geom_slice_angular_origin_present_flag ) {...}

In another example, threshold1 may always be signaled and threshold0 may be signaled conditionally based on the value of a flag.

In another example, the mid-point of the two thresholds may be signaled (m), and a distance of the mid-point from either threshold (w) may be signaled; the two thresholds may be then derived as m−w and m+w. These values may be signaled using fixed length or variable length coding.

In another example, G-PCC encoder200and G-PCC decoder300may code data for these thresholds in GPS level, with a possibility of overriding/refining the thresholds in the GDH level.

In another example, G-PCC encoder200and G-PCC decoder300may code these thresholds together with the global motion information (rotation and translation factors).

In another example, G-PCC encoder200and G-PCC decoder300may code the thresholds in a separate parameter set, such as a parameter set dedicated to motion-related parameters.

In some examples, in addition or in the alternative to the techniques discussed above, G-PCC encoder200and G-PCC decoder300may be configured to implicitly classify points, e.g., as object or ground points, through coding points in slices corresponding to the classes. For example, if the point cloud includes object and ground (or road) point classes, G-PCC encoder200and G-PCC decoder300may code an object slice including a first subset of points that are all classified as object points, and a ground or road slice including a second subset of points that are all classified as ground or road points. More than two classes may be used in this way. In general, G-PCC encoder200and G-PCC decoder300may be configured to determine that there is one slice for each class of points, and that all points within a given slice are to be classified according to the corresponding class for the given slice. An explicit classification algorithm is not necessary in this example, which may reduce computations to be performed by G-PCC encoder200and G-PCC decoder300.

More generally, G-PCC encoder200and G-PCC decoder300may be configured to perform the techniques below, alone or in any combination with the various other techniques of this disclosure:1. Classification (or partitioning) of points of a point cloud into M groups. G-PCC encoder200and G-PCC decoder300may be configured according to one of the techniques of this disclosure, or other means to achieve the classification of the points into the M groups.a. Examples of groups include road, divider, nearby cars or vehicles, buildings, signs, traffic lights, pedestrians, etc. Note that each car/vehicle/building/etc. may be classified as a separate group.b. Groups may include points that represent an object, or that are spatially adjacent to each other.2. G-PCC encoder200and G-PCC decoder300may specify N slice groups (N<=M). G-PCC encoder200and G-PCC decoder300may associate each of the M groups with one of the N slice groups. G-PCC encoder200and G-PCC decoder300may code points belonging to a slice group together.a. E.g., a “ground” slice group may include points belonging to the “road” and “divider” groups, “static” slice group may include points belonging to “buildings”, and “signs”, and “dynamic” slice group may include groups such as cars/vehicles, or “pedestrians.”b. More generally, G-PCC encoder200and G-PCC decoder300may code one or more groups that share some property into a slice group. For example, groups that may have similar relative motion with respect to the LIDAR sensor/vehicle, may be coded into one slice group.c. In another example, G-PCC encoder200and G-PCC decoder300may be configured to determine that each group of points having a certain property belongs to a separate slice group.d. Points of a group may be associated with more than one slice group (e.g., the points may be repeated).3. G-PCC encoder200and G-PCC decoder300may code points belonging to each slice group in one or more slices.4. G-PCC encoder200and G-PCC decoder300may identify a slice belonging to a slice group based on an index value (e.g., slice index) or a label (slice type or slice group type).a. Each slice group may be associated with a slice type/slice group type which may be signalled in each slice of the slice group.i. For example, an index/label of [0, N−1] may be associated with each of the slice groups and G-PCC encoder200and G-PCC decoder300may code an index/label “i” in a slice that belongs to the i-th slice group (0<=i<=N−1).ii. In another example, a point cloud may have two slice groups S1 and S2, and each slice group may be coded as 3 slices, making a total of 6 slices. Each of the slices of S1 may have slice type 0 and each of the slices of S2 may have slice type 1.b. In another example, each slice may be associated with a slice number of slice index; slice belonging to a particular slice group may be identified with the slice number/index.i. For example, a point cloud may have two slice groups S1 and S2, and each slice group may be coded as 3 slices, making a total of 6 slices. The slices of S1 may have slice numbers 0, 1 and 2, and slices of S2 may have slice numbers 3, 4 and 5.c. In some examples, the slice identifier may be a combination of the slice group identifier/type, and a slice number.i. For example, a point cloud may have two slice groups S1 and S2, and each slice group may be coded as 3 slices, making a total of 6 slices. The slices of S1 may have identifiers (0, 0), (0, 1), (0, 2) where the first number of each tuple is the slice type, and the second number is the slice number within the slice group. Similarly slices of S2 may have identifiers (1, 0), (1, 1), (1, 2).d. The slice type, slice group type, slice number, of slice identifier may be signalled in the slice.5. G-PCC encoder200and G-PCC decoder300may code data referring to slices for prediction. A slice may refer to another slice for prediction. The reference slice may belong to the same picture (intra prediction) or another picture (inter prediction).a. G-PCC encoder200and G-PCC decoder300may identify the reference slice using one or more of the following:i. A reference frame number or frame counterii. A reference slice identifier (slice type/group type, slice number, slice identifier, etc.)b. In some examples, G-PCC encoder200and G-PCC decoder300may be configured according to a restriction that a slice may only refer to other slices belonging to the same slice type/slice group type. In this case, a reference slice type/slice group type need not be signalled.c. In another example, a slice may be allowed to refer all points belonging to a frame or a slice group; in this case, a reference slice number may not be signalled as all the slices of a frame/slice group may be referred for prediction.d. In another example, two or more slice identifiers may be signalled identifying that plurality of slices that may referred for prediction.6. G-PCC encoder200and G-PCC decoder300may associate a first set of motion parameters for each point; the motion parameters may be used to compensate the position of the point; this compensated position may be used as a reference for prediction.a. In one example, motion parameters associated with a point may be the motion parameters associated with a slice containing the point.b. In one example, motion parameters associated with a slice may be the motion parameters associated with a slice group containing the slice.c. In one example, the motion parameters associated with a slice group may be the motion parameters associated with the frame containing the slice group.d. The motion parameters may be signalled in a parameter set such as SPS, GPS, etc., slice header, or other parts of the bitstream.e. The above description refers to motion parameters, but this may apply to any set of motion parameters (e.g., rotation matrix/parameters, translation vector/parameters, etc.)f. In some examples, motion parameters used to apply motion compensation for points in a reference frame may be signalled in the current frame, or a frame that is not the reference frame. E.g., if frame 1 uses points from frame 0 for prediction, then the motion parameters that apply to points in frame 0 may be signalled with frame 1.g. In one example, a reference index to the slice/slice group of a reference frame may be signalled in the current frame (in a parameter set or a slice or other syntax structure).i. In one example, one or more tuples (motion parameters, a reference index) may be signalled with a current frame (or slice), where the reference index identifies the points in the reference frame (slice/slice group/region) to which the respective motion parameters apply.h. In one example, the motion parameters may be a set of global motion parameter that apply to all points in a slice, slice group, region, or frame.

One or more of the techniques of this disclosure may also apply to attributes, e.g., in addition or in the alternative to applying to points.

In some examples, G-PCC encoder200and G-PCC decoder300may be configured to specify one or more regions within a point cloud. G-PCC encoder200and G-PCC decoder300may further associate motion parameters with each region. G-PCC encoder200and G-PCC decoder300may code data in the bitstream representing the motion parameters associated with a region. G-PCC encoder200and G-PCC decoder300may use the motion parameters to compensate positions of points. G-PCC encoder200and G-PCC decoder300may use the compensated points as reference/prediction for coding the position of a point in a current frame. In some cases, the use of regions (compared with slices) for classification may achieve better compression performance, because G-PCC encoder200and G-PCC decoder300may code points belonging to different regions together.1. G-PCC encoder200and G-PCC decoder300may code data representing one or more regions in a point cloud.a. G-PCC encoder200and G-PCC decoder300may code a value N representing the number of regions, as well as data representing parameters that specify each of the N regions.i. In some examples, N may be restricted to be within a certain value range (e.g., N may be constrained to less than a fixed value, such as 10).b. G-PCC encoder200and G-PCC decoder300may code the parameter of each region in the bitstream. In some examples, a region may be specified using one or more of the following parameters:i. An upper bound and lower bound for x, y, and z coordinates defining the region (or any other coordinate system used to code the point cloud).ii. In some examples, one or more of upper or lower bound may not be specified; in this case, G-PCC encoder200and G-PCC decoder300may use default values appropriate to the coordinate and the coordinate system as an inferred value.1. For example, in a spherical domain (r, phi, laserId), if bounds for phi are not signalled, then the upper and lower bound may be inferred to correspond to 360 degrees and 0 degrees, respectively.2. Motion parameters may be associated with each region; motion compensation may be applied to one or more points belonging to the region to obtain compensated position/points; compensated positions/points may be used as reference for prediction of points in a current points cloud frame.a. One or more methods disclosed in this disclosure of signalling motion parameters may be applied to signal the motion parameters of each region. For example, G-PCC encoder200and G-PCC decoder300may code motion parameters for each region in a parameter set (e.g., SPS, GPS), or other parts of the bitstream (e.g., slice header, or a separate syntax structure).

G-PCC encoder200and G-PCC decoder300may be configured to perform any of the various techniques of this disclosure in various combination. For example, motion parameters for a reference frame may be specified in terms of regions, whereas one or more slice groups may be specified for the current frame; a slice group may be associated with a region (explicitly or implicitly) and reference points from region may be used to predict points of the slice group. In another example, points in a region may be coded as a slice or a slice group.

FIG.12is a graph460illustrating an example derivation of thresholds using a histogram according to the techniques of this disclosure. Graph460represents an example histogram for collected heights (z-values) of point cloud data. G-PCC encoder200may calculate thresholds z_bottom462and z_top464using the histogram.

In an example implementation, G-PCC encoder200may downscale the cloud (sub-sample) with the size of hist_bin_size, which may be defined as follows:

hist_bin⁢_size=int⁡((max_box⁢_t-min_box⁢_t)/hist_scale)
where max_box_t and min_box_t is the range of z values in the cloud, which will be used to get the thresholds. Max_box_t may be lower than the maximum value of z in the cloud and min_box_t may be higher than the minimum values of z in the cloud.

Next, G-PCC encoder200may derive the histogram of the points with z-values in the range min_box_t to max_box_t as follows (which is example Python code, although other implementations in other languages or in hardware may also be used):

n,bins=np.histogram(source_points⁢_ori,hist_bin⁢_size,(min_box⁢_t,max_box⁢_t))

In this example, np is the representative of numpy library (numpy.org), and source_points_ori is the set of the points with z values being in the range min_box_t to max_box_t.

After this, G-PCC encoder200may calculate the standard deviation (std)466of the histogram, e.g., according to the following Python code (although other implementations in other languages or in hardware may also be used):

mids=0.5*(bins[1:]+bins[:-1])⁢probs=n/np.sum(n)⁢mean=np.sum(probs*mids)⁢std=np.sqrt⁡(np.sum(probs*(mids-mean)**2))

Finally, in this example, G-PCC encoder200may derive z_bottom462and z_top464as follows: G-PCC encoder200determines the bin index in the histogram (top_idx_n, bin470in the example ofFIG.12) which has the maximum count of points. G-PCC encoder200determines the thresholds z_top (z_top464) and z_bottom (z_bottom462) by shifting to the right and the left from bin470(that is, the bin that has the maximum count of points) by values related to std, e.g., 1*std466and 1.5*std468in the example ofFIG.12. The following Python code represents an example technique by which the thresholds may be derived:

top_idx⁢_n=np.where(n==n.max())⁢z_top=min⁡(bins[top_idx⁢_n]+w_top*std,max_box⁢_t)⁢z_bottom=max⁡(bins[top_idx⁢_n]-w_bottom*std,max_box⁢_t)
where w_top and w_bottom are predefined positive values.

In the example ofFIG.12, (max_box_t, min_box_t) is set equal to (−500, −4000) for the 100thframe of a collected data set. InFIG.12, (w_top, w_bottom) is set equal to (1, 1.5).

FIG.13is a conceptual diagram illustrating labeling of points in point cloud470into road points474and object points472according to the techniques of this disclosure. An automobile equipped with a LIDAR system (not shown inFIG.13) generally positioned at point476may collect data from a surrounding environment to construct point cloud470. A G-PCC encoder, such as G-PCC encoder200, within the automobile may determine thresholds for classifying points of point cloud470into road points or object points. After determining the threshold (e.g., according to the techniques ofFIG.12), G-PCC encoder200may label points of point cloud470as ground/road points474or object points472.

FIG.13represents a visualization of these sets of points, where object points472are darkly shaded and road points474(also referred to as ground points) are lightly shaded. As can be seen in the example ofFIG.13, lightly shaded road points474are generally spread across an even plane (e.g., the ground or a road on the ground), whereas darkly shaded object points472generally define objects such as fences, signs, buildings, or other objects near the position of the automobile when point cloud470was generated.

FIG.14is a flowchart illustrating an example method of encoding a point cloud according to the techniques of this disclosure. The method ofFIG.14is explained with respect to G-PCC encoder200ofFIGS.1and2. Other G-PCC encoding devices may be configured to perform this or a similar method.

Initially, G-PCC encoder200may obtain a point cloud to be encoded, e.g., current cloud140ofFIG.3. The point cloud may include a set of points, each of which has a geometric position (e.g., expressed in (x, y, z) coordinates) and one or more attributes. G-PCC encoder200may then determine height values of the points in the point cloud (500), e.g., using the z-values of the geometric positions of the points. G-PCC encoder200may then determine top and bottom thresholds (502) and classify ground and object points in the point cloud using the thresholds (504). For example, G-PCC encoder200may determine the thresholds using the techniques discussed above with respect toFIGS.11and12. G-PCC encoder200may then classify points between the top and bottom thresholds as ground points, and other points as object points. G-PCC encoder200may also encode a data structure (e.g., an SPS, GPS, GDH, or the like) including data representative of the top and bottom thresholds. The data structure may conform to the examples of any of Tables 1-3 above.

G-PCC encoder200may then calculate a global motion vector for the object points (506). For example, as shown inFIG.3, G-PCC encoder200may calculate global motion vector132for the set of object points. The global motion vector may generally represent the motion vector that best yields predicted cloud134(that is, that yields a predicted cloud including points that most closely match current cloud140relative to reference cloud130). After obtaining the global motion vector, G-PCC encoder200may generate predicted cloud134using global motion vector132relative to reference cloud130(508).

G-PCC encoder200may then determine contexts for encoding occupancy of nodes of current cloud140by the determined object points using predicted cloud134(510). G-PCC encoder200may further entropy encode data representing occupancy of nodes by object points using the contexts (512). In particular, for a given node of current cloud140, G-PCC encoder200may determine whether a corresponding node (having the same size and position within predicted cloud134) is occupied by at least one object point. If the corresponding node is occupied (i.e., includes at least one object point), G-PCC encoder200may determine a context for encoding a value indicating whether the current node is occupied as having a high likelihood indicating the current node of current cloud140is also occupied. If the corresponding node is not occupied (i.e., does not include any object points), G-PCC encoder200may determine the context for encoding the value indicating whether the current node is occupied as having a high likelihood of indicating that the current node of current cloud140is not occupied.

G-PCC encoder200may then encode the value using the determined context. If the current node was not occupied, G-PCC encoder200may proceed to a new node. On the other hand, if the current node was occupied, G-PCC encoder200may partition the current node into eight sub-nodes and encode occupancy data for each of the eight sub-nodes in the same manner.

G-PCC encoder200may then also separately encode data representing occupancy of nodes by ground points (514). For example, G-PCC encoder200may encode the data representing occupancy of nodes by the ground points using a second different global motion vector, local motion vectors, and/or intra-prediction.

In this manner, the method ofFIG.14represents an example of a method of coding point cloud data, including determining height values of points in a point cloud; classifying the points into a set of ground points or a set of object points according to the height values; and coding the ground points and the object points according to the classifications.

FIG.15is a flowchart illustrating an example method of decoding a point cloud according to the techniques of this disclosure. The method ofFIG.15is explained as being performed by G-PCC decoder300ofFIGS.1and4. However, in other examples, other decoding devices may be configured to perform this or a similar method.

Initially, G-PCC decoder300may determine top and bottom threshold values (520). For example, G-PCC decoder300may decode a data structure (e.g., an SPS, GPS, GDH, or the like) including data representative of the top and bottom thresholds. The data structure may conform to the examples of any of Tables 1-3 above. G-PCC decoder300may further decode a global motion vector for object points (522), that is, points within nodes having height values outside of a range between the top and bottom thresholds. For example, G-PCC decoder300may decode data representing occupancy of nodes above a top threshold and/or below the bottom threshold using the global motion vector, as follows.

G-PCC decoder300may form a predicted cloud (e.g., predicted cloud154ofFIG.5) using the global motion vector (e.g., global motion vector152) relative to reference cloud150(524). G-PCC decoder300may then use points within predicted cloud154to determine contexts for decoding data representative of occupancy of nodes in current cloud160(526). In particular, for a given node of current cloud160, G-PCC decoder300may determine whether a corresponding node (having the same size and position within predicted cloud154) is occupied by at least one object point. If the corresponding node is occupied (i.e., includes at least one object point), G-PCC decoder300may determine a context for decoding a value indicating whether the current node is occupied as having a high likelihood indicating the current node of current cloud160is also occupied. If the corresponding node is not occupied (i.e., does not include any object points), G-PCC decoder300may determine the context for encoding the value indicating whether the current node is occupied as having a high likelihood of indicating that the current node of current cloud160is not occupied.

G-PCC decoder300may then entropy decode data representing occupancy of the nodes using the contexts (528). If the decoded data indicates that the current node was not occupied, G-PCC decoder300may proceed to a new node. On the other hand, if the current node was occupied, G-PCC decoder300may partition the current node into eight sub-nodes and decode occupancy data for each of the eight sub-nodes in the same manner.

G-PCC decoder300may also separately decode data representing occupancy of nodes by ground points (530), e.g., using a different global motion vector, local motion vectors, and/or intra-prediction.

FIG.16is a conceptual diagram illustrating a laser package600, such as a LIDAR sensor or other system that includes one or more lasers, scanning points in 3-dimensional space. Laser package600may correspond to LIDAR380ofFIG.7. Data source104(FIG.1) may include laser package600.

As shown inFIG.16, point clouds can be captured using laser package600, i.e., the sensor scans the points in 3D space. It is to be understood, however, that some point clouds are not generated by an actual LIDAR sensor but may be encoded as if they were. In the example ofFIG.16, laser package600includes a LIDAR head602that includes multiple lasers604A-604E (collectively, “lasers604”) arrayed in a vertical plane at different angles relative to an origin point. Laser package600may rotate around a vertical axis608. Laser package600may use returned laser light to determine the distances and positions of points of the point cloud. Laser beams606A-606E (collectively, “laser beams606”) emitted by lasers604of laser package600may be characterized by a set of parameters. Distances denoted by arrows610,612denotes an example laser correction values for laser604B,604A, respective.

Certain lasers604may generally identify object points, whereas other lasers604may generally identify ground points. Using the techniques of this disclosure, the points may be classified as either ground or object points and encoded or decoded accordingly.

FIG.17is a conceptual diagram illustrating an example range-finding system900that may be used with one or more techniques of this disclosure. In the example ofFIG.17, range-finding system900includes an illuminator902and a sensor904. Illuminator902may emit light906. In some examples, illuminator902may emit light906as one or more laser beams. Light906may be in one or more wavelengths, such as an infrared wavelength or a visible light wavelength. In other examples, light906is not coherent, laser light. When light906encounters an object, such as object908, light906creates returning light910. Returning light910may include backscattered and/or reflected light. Returning light910may pass through a lens911that directs returning light910to create an image912of object908on sensor904. Sensor904generates signals914based on image912. Image912may comprise a set of points (e.g., as represented by dots in image912ofFIG.17).

In some examples, illuminator902and sensor904may be mounted on a spinning structure so that illuminator902and sensor904capture a 360-degree view of an environment. In other examples, range-finding system900may include one or more optical components (e.g., mirrors, collimators, diffraction gratings, etc.) that enable illuminator902and sensor904to detect objects within a specific range (e.g., up to 360-degrees). Although the example ofFIG.17only shows a single illuminator902and sensor904, range-finding system900may include multiple sets of illuminators and sensors.

In some examples, illuminator902generates a structured light pattern. In such examples, range-finding system900may include multiple sensors904upon which respective images of the structured light pattern are formed. Range-finding system900may use disparities between the images of the structured light pattern to determine a distance to an object908from which the structured light pattern backscatters. Structured light-based range-finding systems may have a high level of accuracy (e.g., accuracy in the sub-millimeter range), when object908is relatively close to sensor904(e.g., 0.2 meters to 2 meters). This high level of accuracy may be useful in facial recognition applications, such as unlocking mobile devices (e.g., mobile phones, tablet computers, etc.) and for security applications.

In some examples, range-finding system900is a time of flight (ToF)-based system. In some examples where range-finding system900is a ToF-based system, illuminator902generates pulses of light. In other words, illuminator902may modulate the amplitude of emitted light906. In such examples, sensor904detects returning light910from the pulses of light906generated by illuminator902. Range-finding system900may then determine a distance to object908from which light906backscatters based on a delay between when light906was emitted and detected and the known speed of light in air). In some examples, rather than (or in addition to) modulating the amplitude of the emitted light906, illuminator902may modulate the phase of the emitted light1404. In such examples, sensor904may detect the phase of returning light910from object908and determine distances to points on object908using the speed of light and based on time differences between when illuminator902generated light906at a specific phase and when sensor904detected returning light910at the specific phase.

In other examples, a point cloud may be generated without using illuminator902. For instance, in some examples, sensor904of range-finding system900may include two or more optical cameras. In such examples, range-finding system900may use the optical cameras to capture stereo images of the environment, including object908. Range-finding system900(e.g., point cloud generator920) may then calculate the disparities between locations in the stereo images. Range-finding system900may then use the disparities to determine distances to the locations shown in the stereo images. From these distances, point cloud generator920may generate a point cloud.

Sensors904may also detect other attributes of object908, such as color and reflectance information. In the example ofFIG.17, a point cloud generator920may generate a point cloud based on signals918generated by sensor904. Range-finding system900and/or point cloud generator920may form part of data source104(FIG.1).

FIG.18is a conceptual diagram illustrating an example vehicle-based scenario in which one or more techniques of this disclosure may be used. In the example ofFIG.18, a vehicle1000includes a laser package1002, such as a LIDAR system. Laser package1002may be implemented in the same manner as laser package600(FIG.16). Although not shown in the example ofFIG.18, vehicle1000may also include a data source, such as data source104(FIG.1), and a G-PCC encoder, such as G-PCC encoder200(FIG.1). In the example ofFIG.18, laser package1002emits laser beams1004that reflect off pedestrians1006or other objects in a roadway. The data source of vehicle1000may generate a point cloud based on signals generated by laser package1002. The G-PCC encoder of vehicle1000may encode the point cloud to generate bitstreams1008, such as the geometry bitstream ofFIG.2and the attribute bitstream ofFIG.2. Bitstreams1008may include many fewer bits than the unencoded point cloud obtained by the G-PCC encoder. An output interface of vehicle1000(e.g., output interface108(FIG.1) may transmit bitstreams1008to one or more other devices. Thus, vehicle1000may be able to transmit bitstreams1008to other devices more quickly than the unencoded point cloud data. Additionally, bitstreams1008may require less data storage capacity.

The techniques of this disclosure may further reduce the number of bits in bitstreams1008. For instance, separately encoding object points from ground points, e.g., using global motion information for the object points, may reduce the number of bits in bitstreams1008associated with the object points.

In the example ofFIG.18, vehicle1000may transmit bitstreams1008to another vehicle1010. Vehicle1010may include a G-PCC decoder, such as G-PCC decoder300(FIG.1). The G-PCC decoder of vehicle1010may decode bitstreams1008to reconstruct the point cloud. Vehicle1010may use the reconstructed point cloud for various purposes. For instance, vehicle1010may determine based on the reconstructed point cloud that pedestrians1006are in the roadway ahead of vehicle1000and therefore start slowing down, e.g., even before a driver of vehicle1010realizes that pedestrians1006are in the roadway. Thus, in some examples, vehicle1010may perform an autonomous navigation operation, generate a notification or warning, or perform another action based on the reconstructed point cloud.

Additionally or alternatively, vehicle1000may transmit bitstreams1008to a Server system1012. Server system1012may use bitstreams1008for various purposes. For example, server system1012may store bitstreams1008for subsequent reconstruction of the point clouds. In this example, server system1012may use the point clouds along with other data (e.g., vehicle telemetry data generated by vehicle1000) to train an autonomous driving system. In other example, server system1012may store bitstreams1008for subsequent reconstruction for forensic crash investigations (e.g., if vehicle1000collides with pedestrians1006).

FIG.19is a conceptual diagram illustrating an example extended reality system in which one or more techniques of this disclosure may be used. Extended reality (XR) is a term used to cover a range of technologies that includes augmented reality (AR), mixed reality (MR), and virtual reality (VR). In the example ofFIG.19, a first user1100is located in a first location1102. User1100wears an XR headset1104. As an alternative to XR headset1104, user1100may use a mobile device (e.g., mobile phone, tablet computer, etc.). XR headset1104includes a depth detection sensor, such as a LIDAR system, that detects positions of points on objects1106at location1102. A data source of XR headset1104may use the signals generated by the depth detection sensor to generate a point cloud representation of objects1106at location1102. XR headset1104may include a G-PCC encoder (e.g., G-PCC encoder200ofFIG.1) that is configured to encode the point cloud to generate bitstreams1108.

The techniques of this disclosure may further reduce the number of bits in bitstreams1108. For instance, separately encoding object points from ground points, e.g., using common global motion information for the object points, may reduce the number of bits in bitstreams1108associated with the third laser angle.

XR headset1104may transmit bitstreams1108(e.g., via a network such as the Internet) to an XR headset1110worn by a user1112at a second location1114. XR headset1110may decode bitstreams1108to reconstruct the point cloud. XR headset1110may use the point cloud to generate an XR visualization (e.g., an AR, MR, VR visualization) representing objects1106at location1102. Thus, in some examples, such as when XR headset1110generates a VR visualization, user1112at location1114may have a 3D immersive experience of location1102. In some examples, XR headset1110may determine a position of a virtual object based on the reconstructed point cloud. For instance, XR headset1110may determine, based on the reconstructed point cloud, that an environment (e.g., location1102) includes a flat surface and then determine that a virtual object (e.g., a cartoon character) is to be positioned on the flat surface. XR headset1110may generate an XR visualization in which the virtual object is at the determined position. For instance, XR headset1110may show the cartoon character sitting on the flat surface.

FIG.20is a conceptual diagram illustrating an example mobile device system in which one or more techniques of this disclosure may be used. In the example ofFIG.20, a mobile device1200, such as a mobile phone or tablet computer, includes a depth detection sensor, such as a LIDAR system, that detects positions of points on objects1202in an environment of mobile device1200. A data source of mobile device1200may use the signals generated by the depth detection sensor to generate a point cloud representation of objects1202. Mobile device1200may include a G-PCC encoder (e.g., G-PCC encoder200ofFIG.1) that is configured to encode the point cloud to generate bitstreams1204. In the example ofFIG.20, mobile device1200may transmit bitstreams to a remote device1206, such as a server system or other mobile device. Remote device1206may decode bitstreams1204to reconstruct the point cloud. Remote device1206may use the point cloud for various purposes. For example, remote device1206may use the point cloud to generate a map of environment of mobile device1200. For instance, remote device1206may generate a map of an interior of a building based on the reconstructed point cloud. In another example, remote device1206may generate imagery (e.g., computer graphics) based on the point cloud. For instance, remote device1206may use points of the point cloud as vertices of polygons and use color attributes of the points as the basis for shading the polygons. In some examples, remote device1206may perform facial recognition using the point cloud.

The following clauses represent various examples of techniques described in this disclosure:Clause 1: A method of coding point cloud data, the method comprising: determining height values of points in a point cloud; classifying the points into a set of ground points or a set of object points according to the height values; and coding the ground points and the object points according to the classifications.Clause 2: The method of clause 1, wherein classifying the points comprises: determining a top threshold and a bottom threshold; classifying points having height values between the top threshold and the bottom threshold into the set of ground points; and classifying points having height values above the top threshold or below the bottom threshold into the set of object points.Clause 3: The method of any of clauses 1 and 2, wherein the top threshold comprises z_maxiand the bottom threshold comprises z_miniof an ithvalue range {(x_mini, x_maxi), (y_mini, y_maxi), (z_mini, z_maxi)}.Clause 4: The method of clause 3, wherein the ithvalue range comprises an ithvalue range of N value ranges.Clause 5: The method of any of clauses 3 and 4, wherein x_miniand y_minihave values of negative infinity, and x_maxiand y_maxihave values of infinity.Clause 6: The method of any of clauses 2-5, wherein coding the ground points and the object points further comprises: quantizing the ground points and the object points by a scaling factor; and quantizing the top threshold and the bottom threshold by the scaling factor.Clause 7: The method of any of clauses 2-6, wherein coding the object points comprises: deriving a set of global motion for the object points; and predicting the object points using the set of global motion.Clause 8: The method of clause 7, wherein deriving the set of global motion comprises deriving the set of global motion only from the object points.Clause 9: The method of any of clauses 7 and 8, wherein the set of global motion comprises a first set of global motion, and wherein coding the ground points comprises: deriving a second set of global motion for the ground points; and predicting the ground points using the second set of global motion.Clause 10: The method of clause 9, wherein deriving the second set of global motion comprises deriving the second set of global motion only from the ground points.Clause 11: The method of any of clauses 7-10, wherein deriving the set of global motion comprises deriving a rotation matrix and a translation vector, and wherein coding the object points comprises applying the rotation matrix and the translation vector to reference points of a reference frame.Clause 12: The method of clause 11, wherein coding the object points further comprises: determining local node motion vectors of nodes of a prediction tree, the nodes including respective sets of reference points of the reference frame; and applying the local node motion vectors to the nodes.Clause 13: The method of any of clauses 2-12, wherein determining the top threshold and the bottom threshold comprises determining the top threshold and the bottom threshold for a group of pictures (GOP) including a plurality of frames including the point cloud.Clause 14: The method of any of clauses 2-12, wherein determining the top threshold and the bottom threshold comprises determining the top threshold and the bottom threshold for a sequence parameter set (SPS) corresponding to a plurality of frames including the point cloud.Clause 15: The method of any of clauses 13 and 14, wherein determining the top threshold and the bottom threshold comprises determining the top threshold and the bottom threshold for an ordinal first frame of the plurality of frames.Clause 16: The method of any of clauses 13 and 14, wherein determining the top threshold and the bottom threshold comprises determining the top threshold and the bottom threshold as a weighted average of threshold for the plurality of frames.Clause 17: The method of any of clauses 2-16, further comprising coding a global parameter set (GPS) including data representing at least one of the top threshold or the bottom threshold.Clause 18: The method of clause 17, wherein coding the GPS comprises coding a value for the top threshold and a flag indicating whether data is to be coded for the bottom threshold.Clause 19: The method of any of clauses 17 and 18, wherein coding the data for the at least one of the top threshold or the bottom threshold comprises: coding a value for a geom_globmotion_threshold0 representing the top threshold; and coding a value for a geom_globmotion_threshold1 representing the bottom threshold.Clause 20: The method of any of clauses 17-19, wherein coding the data representing the at least one of the top threshold or the bottom threshold comprises coding the data representing the at least one of the top threshold or the bottom threshold using respective unsigned integer 0thorder Exp-Golomb values.Clause 21: The method of any of clauses 17-19, wherein coding the data representing the at least one of the top threshold or the bottom threshold comprises coding the data representing the at least one of the top threshold or the bottom threshold using respective signed integer 0thorder Exp-Golomb values.Clause 22: The method of any of clauses 17-19, wherein coding the data representing the at least one of the top threshold or the bottom threshold comprises coding the data representing the at least one of the top threshold or the bottom threshold using respective signed fixed length values, the method further comprising coding data representing a number of bits assigned to the at least one of the top threshold or the bottom threshold.Clause 23: The method of any of clauses 17-22, wherein coding the data representing the at least one of the top threshold or the bottom threshold comprises: coding data representing a midpoint between the top threshold and the bottom threshold; and coding data representing a distance from the midpoint to the top threshold and the bottom threshold.Clause 24: The method of any of clauses 17-23, further comprising coding a geometry data unit header (GDH) including data that overrides or refines the data of the GPS for the at least one of the top threshold or the bottom threshold.Clause 25: The method of any of clauses 2-24, wherein determining the top threshold and the bottom threshold comprises: determining a maximum histogram height value, max_box_t; determining a minimum histogram height value, min_box_t; determining a histogram scale value, hist_scale; determining a histogram bin size value, hist_bin_size, according to int((max_box_t−min_box_t)/hist_scale); generating a histogram of the points with height values in the range from min_box_t to max_box_t; calculating a standard deviation of the histogram; determining a bin having a maximum number of height values in the histogram; and determining the top threshold and the bottom threshold according to offsets from the bin having the maximum number of height values, the offsets being defined according to respective multiples of the standard deviation.Clause 26: A method of coding point cloud data, the method comprising: determining a first class associated with a first slice of a frame of point cloud data, the first slice including first one or more points; determining that the first one or more points correspond to the first class; coding the first one or more points according to the determination that the first one or more points correspond to the first class; determining a second class associated with a second slice of the frame of point cloud data, the second slice including second one or more points; determining that the second one or more points correspond to the second class; and coding the second one or more points according to the determination that the second one or more points correspond to the second class.Clause 27: A method comprising a combination of the method of any of clauses 1-25 and the method of clause 26.Clause 28: The method of any of clauses 26 and 27, further comprising: determining a third class associated with a third slice of the frame of point cloud data, the third slice including third one or more points; determining that the third one or more points correspond to the third class; and coding the third one or more points according to the determination that the third one or more points correspond to the third class.Clause 29: The method of any of clauses 26-28, wherein the first class and second class comprise at least one of road, divider, nearby vehicle, building, sign, traffic lights, or pedestrian.Clause 30: The method of any of clauses 26-29, further comprising coding data representing a slice group for the first slice, wherein coding the first one or more points comprises coding the first one or more points and other points included in one or more other slices corresponding to the slice group together.Clause 31: The method of clause 30, further comprising: determining a third class associated with a third slice of the frame of point cloud data, the third slice being one of the one or more other slices corresponding to the slice group; and coding third one or more points of the third slice together with the first one or more points.Clause 32: The method of any of clauses 30 and 31, further comprising coding index values for each of the slices representing a corresponding slice group.Clause 33: The method of any of clauses 26-32, wherein coding the first one or more points comprises predicting at least one of the first one or more points from third one or more points of a third slice.Clause 34: The method of clause 33, wherein the frame comprises a first frame, and the third slice forms part of a second frame different than the first frame.Clause 35: The method of any of clauses 26-34, further comprising determining respective motion parameters for each of the first one or more points and the second one or more points.Clause 36: A method of coding point cloud data, the method comprising: determining one or more regions of a frame of point cloud data; and for each of the regions: coding data representing respective motion parameters for the region; and coding points of the region using the respective motion parameters for the region.Clause 37: A method comprising a combination of the method of any of clauses 1-35 and the method of clause 36.Clause 38: The method of any of clauses 36 and 37, further comprising coding data representing a number of the regions included in the frame.Clause 39: The method of any of clauses 36-38, further comprising coding parameters specifying each of the regions of the frame.Clause 40: The method of clause 39, wherein the parameters include at least one of an upper bound or a lower bound for one or more of an x-coordinate of the region, a y-coordinate of the region, or a z-coordinate of the region.Clause 41: The method of any of clauses 39 and 40, further comprising determining default values for one or more coordinates of the region.Clause 42: The method of any of clauses 36-41, wherein coding the points of the region comprises applying motion compensation to the points of the region using the respective motion parameters.Clause 43: A method of coding point cloud data, the method comprising: determining height values of points in a point cloud; classifying the points into a set of ground points or a set of object points according to the height values; and coding the ground points and the object points according to the classifications.Clause 44: The method of clause 43, wherein classifying the points comprises: determining a top threshold and a bottom threshold; classifying points having height values between the top threshold and the bottom threshold into the set of ground points; and classifying points having height values above the top threshold or below the bottom threshold into the set of object points.Clause 45: The method of clause 43, wherein the top threshold comprises z_maxiand the bottom threshold comprises z_miniof an ithvalue range {(x_mini, x_maxi), (y_mini, y_maxi), (z_mini, z_maxi)}.Clause 46: The method of clause 45, wherein the ithvalue range comprises an ithvalue range of N value ranges.Clause 47: The method of clause 45, wherein x_miniand y_minihave values of negative infinity, and x_maxiand y_maxihave values of infinity.Clause 48: The method of clause 44, wherein coding the ground points and the object points further comprises: quantizing the ground points and the object points by a scaling factor; and quantizing the top threshold and the bottom threshold by the scaling factor.Clause 49: The method of clause 44, wherein coding the object points comprises: deriving a set of global motion for the object points; and predicting the object points using the set of global motion.Clause 50: The method of clause 49, wherein deriving the set of global motion comprises deriving the set of global motion only from the object points.Clause 51: The method of clause 50, wherein the set of global motion comprises a first set of global motion, and wherein coding the ground points comprises: deriving a second set of global motion for the ground points; and predicting the ground points using the second set of global motion.Clause 52: The method of clause 51, wherein deriving the second set of global motion comprises deriving the second set of global motion only from the ground points.Clause 53: The method of clause 49, wherein deriving the set of global motion comprises deriving a rotation matrix and a translation vector, and wherein coding the object points comprises applying the rotation matrix and the translation vector to reference points of a reference frame.Clause 54: The method of clause 53, wherein coding the object points further comprises: determining local node motion vectors of nodes of a prediction tree, the nodes including respective sets of reference points of the reference frame; and applying the local node motion vectors to the nodes.Clause 55: The method of clause 44, wherein determining the top threshold and the bottom threshold comprises determining the top threshold and the bottom threshold for a group of pictures (GOP) including a plurality of frames including the point cloud.Clause 56: The method of clause 44, wherein determining the top threshold and the bottom threshold comprises determining the top threshold and the bottom threshold for a sequence parameter set (SPS) corresponding to a plurality of frames including the point cloud.Clause 57: The method of clause 56, wherein determining the top threshold and the bottom threshold comprises determining the top threshold and the bottom threshold for an ordinal first frame of the plurality of frames.Clause 58: The method of clause 56, wherein determining the top threshold and the bottom threshold comprises determining the top threshold and the bottom threshold as a weighted average of threshold for the plurality of frames.Clause 59: The method of clause 44, further comprising coding a global parameter set (GPS) including data representing at least one of the top threshold or the bottom threshold.Clause 60: The method of clause 59, wherein coding the GPS comprises coding a value for the top threshold and a flag indicating whether data is to be coded for the bottom threshold.Clause 61: The method of clause 59, wherein coding the data for the at least one of the top threshold or the bottom threshold comprises: coding a value for a geom_globmotion_threshold0 representing the top threshold; and coding a value for a geom_globmotion_threshold1 representing the bottom threshold.Clause 62: The method of clause 59, wherein coding the data representing the at least one of the top threshold or the bottom threshold comprises coding the data representing the at least one of the top threshold or the bottom threshold using respective unsigned integer 0thorder Exp-Golomb values.Clause 63: The method of clause 59, wherein coding the data representing the at least one of the top threshold or the bottom threshold comprises coding the data representing the at least one of the top threshold or the bottom threshold using respective signed integer 0thorder Exp-Golomb values.Clause 64: The method of clause 59, wherein coding the data representing the at least one of the top threshold or the bottom threshold comprises coding the data representing the at least one of the top threshold or the bottom threshold using respective signed fixed length values, the method further comprising coding data representing a number of bits assigned to the at least one of the top threshold or the bottom threshold.Clause 65: The method of clause 59, wherein coding the data representing the at least one of the top threshold or the bottom threshold comprises: coding data representing a midpoint between the top threshold and the bottom threshold; and coding data representing a distance from the midpoint to the top threshold and the bottom threshold.Clause 66: The method of clause 59, further comprising coding a geometry data unit header (GDH) including data that overrides or refines the data of the GPS for the at least one of the top threshold or the bottom threshold.Clause 67: The method of clause 44, wherein determining the top threshold and the bottom threshold comprises: determining a maximum histogram height value, max_box_t; determining a minimum histogram height value, min_box_t; determining a histogram scale value, hist_scale; determining a histogram bin size value, hist_bin_size, according to int((max_box_t−min_box_t)/hist_scale); generating a histogram of the points with height values in the range from min_box_t to max_box_t; calculating a standard deviation of the histogram; determining a bin having a maximum number of height values in the histogram; and determining the top threshold and the bottom threshold according to offsets from the bin having the maximum number of height values, the offsets being defined according to respective multiples of the standard deviation.Clause 68: A method of coding point cloud data, the method comprising: determining a first class associated with a first slice of a frame of point cloud data, the first slice including first one or more points; determining that the first one or more points correspond to the first class; coding the first one or more points according to the determination that the first one or more points correspond to the first class; determining a second class associated with a second slice of the frame of point cloud data, the second slice including second one or more points; determining that the second one or more points correspond to the second class; and coding the second one or more points according to the determination that the second one or more points correspond to the second class.Clause 69: The method of clause 68, further comprising: determining a third class associated with a third slice of the frame of point cloud data, the third slice including third one or more points; determining that the third one or more points correspond to the third class; and coding the third one or more points according to the determination that the third one or more points correspond to the third class.Clause 70: The method of clause 68, wherein the first class and second class comprise at least one of road, divider, nearby vehicle, building, sign, traffic lights, or pedestrian.Clause 71: The method of clause 68, further comprising coding data representing a slice group for the first slice, wherein coding the first one or more points comprises coding the first one or more points and other points included in one or more other slices corresponding to the slice group together.Clause 72: The method of clause 71, further comprising: determining a third class associated with a third slice of the frame of point cloud data, the third slice being one of the one or more other slices corresponding to the slice group; and coding third one or more points of the third slice together with the first one or more points.Clause 73: The method of clause 71, further comprising coding index values for each of the slices representing a corresponding slice group.Clause 74: The method of clause 68, wherein coding the first one or more points comprises predicting at least one of the first one or more points from third one or more points of a third slice.Clause 75: The method of clause 74, wherein the frame comprises a first frame, and the third slice forms part of a second frame different than the first frame.Clause 76: The method of any of clauses 68, further comprising determining respective motion parameters for each of the first one or more points and the second one or more points.Clause 77: A method of coding point cloud data, the method comprising: determining one or more regions of a frame of point cloud data; and for each of the regions: coding data representing respective motion parameters for the region; and coding points of the region using the respective motion parameters for the region.Clause 78: The method of clause 77, further comprising coding data representing a number of the regions included in the frame.Clause 79: The method of clause 77, further comprising coding parameters specifying each of the regions of the frame.Clause 80: The method of clause 79, wherein the parameters include at least one of an upper bound or a lower bound for one or more of an x-coordinate of the region, a y-coordinate of the region, or a z-coordinate of the region.Clause 81: The method of clause 79, further comprising determining default values for one or more coordinates of the region.Clause 82: The method of clause 77, wherein coding the points of the region comprises applying motion compensation to the points of the region using the respective motion parameters.Clause 83: The method of any of clauses 1-82, wherein coding comprises decoding.Clause 84: The method of any of clauses 1-83, wherein coding comprises encoding.Clause 85: A device for decoding point cloud data, the device comprising one or more means for performing the method of any of clauses 1-84.Clause 86: The device of clause 85, further comprising a display configured to display the point cloud data.Clause 87: The device of any of clauses 85 and 86, wherein the device comprises one or more of a camera, a computer, a mobile device, a broadcast receiver device, or a set-top box.Clause 88: The device of clause 85-87, further comprising a memory configured to store the point cloud data.Clause 89: A computer-readable storage medium having stored thereon instructions that, when executed, cause a processor to perform the method of any of clauses 1-84.Clause 90: A device for coding point cloud data, the device comprising: means for determining height values of points in a point cloud; classifying the points into a set of ground points or a set of object points according to the height values; and coding the ground points and the object points according to the classifications.Clause 91: The device of clause 90, wherein the means for classifying the points comprises: means for determining a top threshold and a bottom threshold; means for classifying points having height values between the top threshold and the bottom threshold into the set of ground points; and means for classifying points having height values above the top threshold or below the bottom threshold into the set of object points.Clause 92: A device for coding point cloud data, the device comprising: means for determining a first class associated with a first slice of a frame of point cloud data, the first slice including first one or more points; means for determining that the first one or more points correspond to the first class; means for coding the first one or more points according to the determination that the first one or more points correspond to the first class; means for determining a second class associated with a second slice of the frame of point cloud data, the second slice including second one or more points; means for determining that the second one or more points correspond to the second class; and means for coding the second one or more points according to the determination that the second one or more points correspond to the second class.Clause 93: A device for coding point cloud data, the device comprising: means for determining one or more regions of a frame of point cloud data; means for coding data representing respective motion parameters for each of the regions; and means for coding points of each of the regions using the respective motion parameters for the region including the points.Clause 94: A method of coding point cloud data, the method comprising: determining height values of points in a point cloud; classifying the points into a set of ground points or a set of object points according to the height values; and coding the ground points and the object points according to the classifications.Clause 95: The method of clause 94, wherein coding the object points comprises: deriving a set of global motion information for the object points; and predicting the object points using the set of global motion information.Clause 96: The method of clause 95, wherein deriving the set of global motion information comprises deriving the set of global motion information only for the object points.Clause 97: The method of clause 95, wherein the set of global motion information comprises a first set of global motion information, and wherein coding the ground points comprises: deriving a second set of global motion information for the ground points; and predicting the ground points using the second set of global motion information.Clause 98: The method of clause 97, wherein deriving the second set of global motion information comprises deriving the second set of global motion information only for the ground points.Clause 99: The method of clause 95, wherein deriving the set of global motion information comprises deriving a rotation matrix and a translation vector, and wherein coding the object points comprises applying the rotation matrix and the translation vector to reference points of a reference frame.Clause 100: The method of clause 99, wherein coding the object points further comprises: determining local node motion vectors of nodes of a prediction tree, the nodes including respective sets of reference points of the reference frame; and applying the local node motion vectors to the nodes.Clause 101: The method of clause 94, wherein classifying the points comprises: determining a top threshold and a bottom threshold; classifying points having height values between the top threshold and the bottom threshold into the set of ground points; and classifying points having height values above the top threshold or below the bottom threshold into the set of object points.Clause 102: The method of clause 101, wherein the top threshold comprises z_maxiand the bottom threshold comprises z_miniof an ithvalue range {(x_mini, x_maxi), (y_mini, y_maxi), (z_mini, z_maxi)}.Clause 103: The method of clause 102, wherein the ithvalue range comprises an ithvalue range of N value ranges.Clause 104: The method of clause 102, wherein x_miniand y_minihave values of negative infinity, and x_maxiand y_maxihave values of infinity.Clause 105: The method of clause 101, wherein coding the ground points and the object points further comprises: quantizing the ground points and the object points by a scaling factor; and quantizing the top threshold and the bottom threshold by the scaling factor.Clause 106: The method of clause 101, wherein determining the top threshold and the bottom threshold comprises determining the top threshold and the bottom threshold for a group of pictures (GOP) including a plurality of frames including the point cloud.Clause 107: The method of clause 101, wherein determining the top threshold and the bottom threshold comprises determining the top threshold and the bottom threshold for a sequence parameter set (SPS) corresponding to a plurality of frames including the point cloud.Clause 108: The method of clause 106, wherein determining the top threshold and the bottom threshold comprises determining the top threshold and the bottom threshold for an ordinal first frame of the plurality of frames.Clause 109: The method of clause 106, wherein determining the top threshold and the bottom threshold comprises determining the top threshold and the bottom threshold as a weighted average of thresholds for the plurality of frames.Clause 110: The method of clause 101, further comprising coding a data structure including data representing at least one of the top threshold or the bottom threshold.Clause 111: The method of clause 110, wherein coding the data structure comprises coding at least one of a sequence parameter set (SPS), a geometry parameter set (GPS), or a geometry data unit header (GDH).Clause 112: The method of clause 110, wherein coding the data structure comprises coding a value for the top threshold and a flag indicating whether data is to be coded for the bottom threshold.Clause 113: The method of clause 110, wherein coding the data for the at least one of the top threshold or the bottom threshold comprises: coding a value for a geom_globmotion_threshold0 representing the top threshold; and coding a value for a geom_globmotion_threshold1 representing the bottom threshold.Clause 114: The method of clause 110, wherein coding the data representing the at least one of the top threshold or the bottom threshold comprises coding the data representing the at least one of the top threshold or the bottom threshold using respective unsigned integer 0th order Exp-Golomb values.Clause 115: The method of clause 110, wherein coding the data representing the at least one of the top threshold or the bottom threshold comprises coding the data representing the at least one of the top threshold or the bottom threshold using respective signed integer 0th order Exp-Golomb values.Clause 116: The method of clause 110, wherein coding the data representing the at least one of the top threshold or the bottom threshold comprises coding the data representing the at least one of the top threshold or the bottom threshold using respective signed fixed length values, the method further comprising coding data representing a number of bits assigned to the at least one of the top threshold or the bottom threshold.Clause 117: The method of clause 110, wherein coding the data representing the at least one of the top threshold or the bottom threshold comprises: coding data representing a midpoint between the top threshold and the bottom threshold; and coding data representing a distance from the midpoint to the top threshold and the bottom threshold.Clause 118: The method of clause 110, further comprising coding a geometry data unit header (GDH) including data that overrides or refines the data of the data structure for the at least one of the top threshold or the bottom threshold.Clause 119: The method of clause 101, wherein determining the top threshold and the bottom threshold comprises: determining a maximum histogram height value, max_box_t; determining a minimum histogram height value, min_box_t; determining a histogram scale value, hist_scale; determining a histogram bin size value, hist_bin_size, according to int((max_box_t−min_box_t)/hist_scale); generating a histogram of the points with height values in the range from min_box_t to max_box_t; calculating a standard deviation of the histogram; determining a bin having a maximum number of height values in the histogram; and determining the top threshold and the bottom threshold according to offsets from the bin having the maximum number of height values, the offsets being defined according to respective multiples of the standard deviation.Clause 120: A device for coding point cloud data, the device comprising: a memory configured to store data representing points of a point cloud; and one or more processors implemented in circuitry and configured to: determine height values of points in a point cloud; classify the points into a set of ground points or a set of object points according to the height values; and code the ground points and the object points according to the classifications.Clause 121: The device of clause 120, wherein to code the object points, the one or more processors are configured to: derive a set of global motion information for the object points; and predict the object points using the set of global motion information.Clause 122: The device of clause 121, wherein the one or more processors are configured to derive the set of global motion information only for the object points.Clause 123: The device of clause 121, wherein the set of global motion information comprises a first set of global motion information, and wherein to code the ground points, the one or more processors are configured to: derive a second set of global motion information for the ground points; and predict the ground points using the second set of global motion information.Clause 124: The device of clause 123, wherein the one or more processors are configured to derive the second set of global motion information only for the ground points.Clause 125: The device of clause 121, wherein to derive the set of global motion information, the one or more processors are configured to derive a rotation matrix and a translation vector, and wherein to code the object points, the one or more processors are configured to apply the rotation matrix and the translation vector to reference points of a reference frame.Clause 126: The device of clause 125, wherein to code the object points, the one or more processors are further configured to: determine local node motion vectors of nodes of a prediction tree, the nodes including respective sets of reference points of the reference frame; and apply the local node motion vectors to the nodes.Clause 127: The device of clause 120, wherein to classify the points, the one or more processors are configured to: determine a top threshold and a bottom threshold; classify points having height values between the top threshold and the bottom threshold into the set of ground points; and classify points having height values above the top threshold or below the bottom threshold into the set of object points.Clause 128: The device of clause 127, wherein the top threshold comprises z_maxiand the bottom threshold comprises z_miniof an ithvalue range {(x_mini, x_maxi), (y_mini, y_maxi), (z_mini, z_maxi)}.Clause 129: The device of clause 128, wherein the ithvalue range comprises an ithvalue range of N value ranges.Clause 130: The device of clause 128, wherein x_miniand y_minihave values of negative infinity, and x_maxiand y_maxihave values of infinity.Clause 131: The device of clause 127, wherein to classify the ground points and the object points, the one or more processors are further configured to: quantize the ground points and the object points by a scaling factor; and quantize the top threshold and the bottom threshold by the scaling factor.Clause 132: The device of clause 127, wherein the one or more processors are further configured to code a data structure including data representing at least one of the top threshold or the bottom threshold.Clause 133: A method of coding point cloud data, the method comprising: determining height values of points in a point cloud; classifying the points into a set of ground points or a set of object points according to the height values; and coding the ground points and the object points according to the classifications.Clause 134: The method of clause 133, wherein coding the object points comprises: deriving a set of global motion information for the object points; and predicting the object points using the set of global motion information.Clause 135: The method of clause 134, wherein deriving the set of global motion information comprises deriving the set of global motion information only for the object points.Clause 136: The method of any of clauses 134 and 135, wherein the set of global motion information comprises a first set of global motion information, and wherein coding the ground points comprises: deriving a second set of global motion information for the ground points; and predicting the ground points using the second set of global motion information.Clause 137: The method of clause 136, wherein deriving the second set of global motion information comprises deriving the second set of global motion information only for the ground points.Clause 138: The method of any of clauses 134-137, wherein deriving the set of global motion information comprises deriving a rotation matrix and a translation vector, and wherein coding the object points comprises applying the rotation matrix and the translation vector to reference points of a reference frame.Clause 139: The method of clause 138, wherein coding the object points further comprises: determining local node motion vectors of nodes of a prediction tree, the nodes including respective sets of reference points of the reference frame; and applying the local node motion vectors to the nodes.Clause 140: The method of any of clauses 133-139, wherein classifying the points comprises: determining a top threshold and a bottom threshold; classifying points having height values between the top threshold and the bottom threshold into the set of ground points; and classifying points having height values above the top threshold or below the bottom threshold into the set of object points.Clause 141: The method of clause 140, wherein the top threshold comprises z_maxiand the bottom threshold comprises z_miniof an ithvalue range {(x_mini, x_maxi), (y_mini, y_maxi), (z_mini, z_maxi)}.Clause 142: The method of clause 141, wherein the ithvalue range comprises an ithvalue range of N value ranges.Clause 143: The method of any of clauses 141 and 142, wherein x_miniand y_minihave values of negative infinity, and x_maxiand y_maxihave values of infinity.Clause 144: The method of any of clauses 134-143, wherein coding the ground points and the object points further comprises: quantizing the ground points and the object points by a scaling factor; and quantizing the top threshold and the bottom threshold by the scaling factor.Clause 145: The method of any of clause 140-144, wherein determining the top threshold and the bottom threshold comprises determining the top threshold and the bottom threshold for a group of pictures (GOP) including a plurality of frames including the point cloud.Clause 146: The method of any of clauses 140-145, wherein determining the top threshold and the bottom threshold comprises determining the top threshold and the bottom threshold for a sequence parameter set (SPS) corresponding to a plurality of frames including the point cloud.Clause 147: The method of any of clauses 140-144, wherein determining the top threshold and the bottom threshold comprises determining the top threshold and the bottom threshold for an ordinal first frame of the plurality of frames.Clause 148: The method of any of clause 140-146, wherein determining the top threshold and the bottom threshold comprises determining the top threshold and the bottom threshold as a weighted average of thresholds for the plurality of frames.Clause 149: The method of any of clauses 140-148, further comprising coding a data structure including data representing at least one of the top threshold or the bottom threshold.Clause 150: The method of clause 149, wherein coding the data structure comprises coding at least one of a sequence parameter set (SPS), a geometry parameter set (GPS), or a geometry data unit header (GDH).Clause 151: The method of any of clauses 149 and 150, wherein coding the data structure comprises coding a value for the top threshold and a flag indicating whether data is to be coded for the bottom threshold.Clause 152: The method of any of clauses 149-151, wherein coding the data for the at least one of the top threshold or the bottom threshold comprises: coding a value for a geom_globmotion_threshold0 representing the top threshold; and coding a value for a geom_globmotion_threshold1 representing the bottom threshold.Clause 153: The method of any of clauses 149-152, wherein coding the data representing the at least one of the top threshold or the bottom threshold comprises coding the data representing the at least one of the top threshold or the bottom threshold using respective unsigned integer 0th order Exp-Golomb values.Clause 154: The method of any of clauses 149-152, wherein coding the data representing the at least one of the top threshold or the bottom threshold comprises coding the data representing the at least one of the top threshold or the bottom threshold using respective signed integer 0th order Exp-Golomb values.Clause 155: The method of any of clauses 149-152, wherein coding the data representing the at least one of the top threshold or the bottom threshold comprises coding the data representing the at least one of the top threshold or the bottom threshold using respective signed fixed length values, the method further comprising coding data representing a number of bits assigned to the at least one of the top threshold or the bottom threshold.Clause 156: The method of any of clauses 149-155, wherein coding the data representing the at least one of the top threshold or the bottom threshold comprises: coding data representing a midpoint between the top threshold and the bottom threshold; and coding data representing a distance from the midpoint to the top threshold and the bottom threshold.Clause 157: The method of any of clauses 149-156, further comprising coding a geometry data unit header (GDH) including data that overrides or refines the data of the data structure for the at least one of the top threshold or the bottom threshold.Clause 158: The method of any of clauses 133-157, wherein determining the top threshold and the bottom threshold comprises: determining a maximum histogram height value, max_box_t; determining a minimum histogram height value, min_box_t; determining a histogram scale value, hist_scale; determining a histogram bin size value, hist_bin_size, according to int((max_box_t−min_box_t)/hist_scale); generating a histogram of the points with height values in the range from min_box_t to max_box_t; calculating a standard deviation of the histogram; determining a bin having a maximum number of height values in the histogram; and determining the top threshold and the bottom threshold according to offsets from the bin having the maximum number of height values, the offsets being defined according to respective multiples of the standard deviation.Clause 159: A device for coding point cloud data, the device comprising: a memory configured to store data representing points of a point cloud; and one or more processors implemented in circuitry and configured to: determine height values of points in a point cloud; classify the points into a set of ground points or a set of object points according to the height values; and code the ground points and the object points according to the classifications.Clause 160: The device of clause 159, wherein to code the object points, the one or more processors are configured to: derive a set of global motion information for the object points; and predict the object points using the set of global motion information.Clause 161: The device of clause 160, wherein the one or more processors are configured to derive the set of global motion information only for the object points.Clause 162: The device of any of clauses 160 and 161, wherein the set of global motion information comprises a first set of global motion information, and wherein to code the ground points, the one or more processors are configured to: derive a second set of global motion information for the ground points; and predict the ground points using the second set of global motion information.Clause 163: The device of clause 162, wherein the one or more processors are configured to derive the second set of global motion information only for the ground points.Clause 164: The device of any of clauses 160-163, wherein to derive the set of global motion information, the one or more processors are configured to derive a rotation matrix and a translation vector, and wherein to code the object points, the one or more processors are configured to apply the rotation matrix and the translation vector to reference points of a reference frame.Clause 165: The device of clause 164, wherein to code the object points, the one or more processors are further configured to: determine local node motion vectors of nodes of a prediction tree, the nodes including respective sets of reference points of the reference frame; and apply the local node motion vectors to the nodes.Clause 166: The device of any of clauses 159-165, wherein to classify the points, the one or more processors are configured to: determine a top threshold and a bottom threshold; classify points having height values between the top threshold and the bottom threshold into the set of ground points; and classify points having height values above the top threshold or below the bottom threshold into the set of object points.Clause 167: The device of clause 166, wherein the top threshold comprises z_maxiand the bottom threshold comprises z_miniof an ithvalue range {(x_mini, x_maxi), (y_mini, y_maxi), (z_mini, z_maxi)}.Clause 168: The device of clause 167, wherein the ithvalue range comprises an ithvalue range of N value ranges.Clause 169: The device of any of clauses 167 and 168, wherein x_miniand y_minihave values of negative infinity, and x_maxiand y_maxihave values of infinity.Clause 170: The device of any of clauses 166-169, wherein to the ground points and the object points, the one or more processors are further configured to: quantize the ground points and the object points by a scaling factor; and quantize the top threshold and the bottom threshold by the scaling factor.Clause 171: The device of any of clauses 166-170, wherein the one or more processors are further configured to code a data structure including data representing at least one of the top threshold or the bottom threshold.

It is to be recognized that depending on the example, certain acts or events of any of the techniques described herein can be performed in a different sequence, may be added, merged, or left out altogether (e.g., not all described acts or events are necessary for the practice of the techniques). Moreover, in certain examples, acts or events may be performed concurrently, e.g., through multi-threaded processing, interrupt processing, or multiple processors, rather than sequentially.

In one or more examples, the functions described may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium and executed by a hardware-based processing unit. Computer-readable media may include computer-readable storage media, which corresponds to a tangible medium such as data storage media, or communication media including any medium that facilitates transfer of a computer program from one place to another, e.g., according to a communication protocol. In this manner, computer-readable media generally may correspond to (1) tangible computer-readable storage media which is non-transitory or (2) a communication medium such as a signal or carrier wave. Data storage media may be any available media that can be accessed by one or more computers or one or more processors to retrieve instructions, code and/or data structures for implementation of the techniques described in this disclosure. A computer program product may include a computer-readable medium.

By way of example, and not limitation, such computer-readable storage media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage, or other magnetic storage devices, flash memory, or any other medium that can be used to store desired program code in the form of instructions or data structures and that can be accessed by a computer. Also, any connection is properly termed a computer-readable medium. For example, if instructions are transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of medium. It should be understood, however, that computer-readable storage media and data storage media do not include connections, carrier waves, signals, or other transitory media, but are instead directed to non-transitory, tangible storage media. Disk and disc, as used herein, includes compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk and Blu-ray disc, where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media.

Instructions may be executed by one or more processors, such as one or more digital signal processors (DSPs), general purpose microprocessors, application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), or other equivalent integrated or discrete logic circuitry. Accordingly, the terms “processor” and “processing circuitry,” as used herein may refer to any of the foregoing structures or any other structure suitable for implementation of the techniques described herein. In addition, in some aspects, the functionality described herein may be provided within dedicated hardware and/or software modules configured for encoding and decoding, or incorporated in a combined codec. Also, the techniques could be fully implemented in one or more circuits or logic elements.

The techniques of this disclosure may be implemented in a wide variety of devices or apparatuses, including a wireless handset, an integrated circuit (IC) or a set of ICs (e.g., a chip set). Various components, modules, or units are described in this disclosure to emphasize functional aspects of devices configured to perform the disclosed techniques, but do not necessarily require realization by different hardware units. Rather, as described above, various units may be combined in a codec hardware unit or provided by a collection of interoperative hardware units, including one or more processors as described above, in conjunction with suitable software and/or firmware.

Various examples have been described. These and other examples are within the scope of the following claims.