Qualcomm Incorporated

Luong Pham Van

Marta Karczewicz

Geert Van Der Auwera

Adarsh Krishnan Ramasubramonian

Vadim Seregin

Wei-Jung Chien

Bappaditya Ray

Cheng-Teh Hsieh

Han Huang

Dmytro Rusanovskyy

Muhammed Zeyd Coban

Louis Joseph Kerofsky

Thibaud Laurent Biatek

Hilmi Enes Egilmez

Yung-Hsuan Chao

Alican Nalci

Example devices and techniques for coding point cloud data are described. An example device includes memory configured to store the point cloud data and one or more processors communicatively coupled to the memory. The one or more processors are configured to determine least two reference points in a reference point cloud frame of the point cloud data. The one or more processors are configured to apply radius interpolation to the at least two reference points to obtain at least one radius inter predictor for at least one current point in a current point cloud frame of the point cloud data. The one or more processors are configured to code the current point cloud frame based on the at least one radius inter predictor for the at least one current point in the current point cloud frame.

Inter prediction coding with radius interpolation for predictive geometry-based point cloud compression

A G-PCC coder may determine an occupancy of a reference child node in a reference node, wherein the reference node is in a reference frame of point cloud data used for inter prediction of a current node in a current frame of the point cloud data. The G-PCC coder may further determine a context for decoding a current occupancy bit of a current child node of the current node based on the occupancy of the reference child node, and arithmetic decode the current occupancy bit using the context.

Occupancy coding using inter prediction with octree occupancy coding based on dynamic optimal binary coder with update on the fly (OBUF) in geometry-based point cloud compression

An example device for coding point cloud data includes a memory configured to store point cloud data; and one or more processors implemented in circuitry and configured to: determine at least one of 1) that a node of an octree of the point cloud data is not inter predictable or 2) that angular mode is enabled for the node; in response to determining the at least one of 1) that the node is not inter predictable or 2) that angular mode is enabled for the node, determine an inferred direct coding mode (IDCM) mode for the node; and code occupancy data of the node using the determined IDCM mode.

Coding point cloud data using direct mode for inter-prediction in G-PCC

A device for encoding or decoding point cloud data may be configured to, responsive to determining to predict a current point in a current frame using an inter prediction mode: identify a reference frame; derive one or more motion compensated reference points from the reference frame; selectively resample points in the reference frame based on the one or more motion compensated reference points to generate a resampled reference frame; and inter predict the current point in the current frame based on the resampled reference frame.

Inter prediction coding for geometry point cloud compression

An example device for coding (encoding or decoding) video data includes a memory configured to store video data; and one or more processors implemented in circuitry and configured to: determine a first number of neighboring blocks to a current block of the video data that are intra-predicted; determine a second number of the neighboring blocks that are inter-predicted; determine a first weight value to be applied to intra-prediction samples of an intra-prediction block for the current block; determine a second weight value to be applied to inter-prediction samples of an inter-prediction block for the current block; generate a prediction block for the current block as a weighted combination of the intra-prediction block to which the first weight value is applied and the inter-prediction block to which the second weight value is applied; and code the current block using the prediction block.

Position-dependent intra-inter prediction combination in video coding

A method of decoding point cloud data comprises obtaining a bitstream that is encoded to comply with one or more constraints and decoding the bitstream, wherein decoding the bitstream comprises: determining a residual value of a first component of an attribute of a point; generating a predicted value of a second component of the attribute of the point; and reconstructing the second component as a sum of the predicted value of the second component and a multiplication product of a scale factor for the second component and the residual value of the first component, wherein the constraints include a constraint that limits the first component of the attribute of the point, the residual value of the first component of the attribute of the point, the second component of the attribute, and the residual value of the second component of the attribute of the point to one or more predefined bitdepths.

Residual coding for geometry point cloud compression

An example device for processing a point cloud includes: a memory configured to store at least a portion of the point cloud; and one or more processors implemented in circuitry and configured to: obtain planar information of a reference block of the point cloud; determine, based on the planar information of the reference block, a context; context-adaptive code, based on the context, a syntax element that indicates whether a current node is coded using a planar mode; code, based on the current node being coded using the planar mode, the current node using the planar mode.

Performance improvement of geometry point cloud compression (GPCC) planar mode using inter prediction

A video decoder can be configured to determine that a first block of the video data is encoded in a cross component linear model (CCLM) mode; determine that the first block has a first bit depth; determine that a second block of the video data is encoded in the CCLM mode; determine that the second block has a second bit depth that is different than the first bit depth; and decode the first block and the second block in the CCLM mode using a fixed bit depth, wherein the fixed bit depth is different than at least one of the first bit depth or the second bit depth.

Fixed bit depth processing for cross-component linear model (CCLM) mode in video coding

A device for decoding a bitstream that includes point cloud data is configured to determine an octree that defines an octree-based splitting of a space containing the point cloud, wherein a leaf node of the octree contains one or more points of the point cloud; and directly decode positions of each of the one or more points in the leaf node, wherein to directly decode the positions of each of the one or more points in the leaf node, the one or more processors are further configured to: generate a prediction of the one or more points; and determine the one or more points based on the prediction.

Hybrid-tree coding for inter and intra prediction for geometry coding

A method comprises: for each of a plurality of dimensions: identifying a reference position for the dimension, the reference position for the dimension being a position in a reference frame for the respective dimension, and the reference frame for the respective dimension and a reference frame for at least one other dimension in the plurality of dimensions being different reference frames in a plurality of reference frames; identifying an inter predictor for the respective dimension, wherein a predictor has a coordinate value in the respective dimension corresponding to a coordinate value in the respective dimension of the inter predictor for the respective dimension; and encoding or decoding the current point based on the predictor.

Prediction for geometry point cloud compression

An example of processing a point cloud includes responsive to determining to predict a current point in the point cloud using predictive geometry coding, selecting, from a set of prediction modes, a prediction mode for the current point, wherein the set of prediction modes includes at least an intra prediction mode and an inter prediction mode; and responsive to selecting the inter prediction mode for the current point, predicting the current point of the point cloud using inter prediction.

An example device for coding point cloud data includes a memory configured to store point cloud data; and one or more processors implemented in circuitry and configured to: determine whether inter prediction data is coded for a current node of an octree of the point cloud data; determine whether planar mask data is coded for the current node; when at least one of the inter prediction data or the planar mask data is coded for the current node, avoid coding a single occupancy value for the current node, the single occupancy value indicating whether only a single sub-node of the current node includes a point; and code the current node. The processors may also be configured to determine a context for entropy coding the planar mask data according to planar mask data for a collocated node in a reference frame when the planar mask data is coded.

Occupancy coding using inter prediction in geometry point cloud compression

A device to code a point cloud data that includes 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; code a data structure including data that represents 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; classify points having height values above the top threshold or below the bottom threshold into the set of object points. The one or more processors code the ground points and the object points according to the classifications. The one or more processors code a geometry data unit header that includes data that overrides or refines the data of the data structure for the at least one of the top threshold or the bottom threshold.

Global motion estimation using road and ground object labels for geometry-based point cloud compression

An example method includes determining, for each respective coding block of a plurality of coding blocks of a current coding tree unit (CTU) of video data in a current picture of video data, a respective search area of a plurality of respective search areas, wherein at least one of the plurality of search areas includes samples of the current picture located outside of the current CTU, and wherein at least one of the plurality of search areas does not include samples of the current picture located outside of the current CTU; selecting, for each respective coding block and from within the respective search area for the respective coding block, a respective predictor block of a plurality of predictor blocks; and reconstructing samples of each respective coding block based on samples included in a corresponding predictor block in the plurality of predictor blocks.

Intra block copy prediction restrictions in video coding

Example devices and techniques for coding video data are described. An example device includes memory configured to store the video data and one or more processors implemented in circuitry and communicatively coupled to the memory. The one or more processors being configured to adjust an input bit depth of reconstructed samples of video data to a fixed bit depth, wherein the fixed bit depth is different than the input bit depth. The one or more processors are configured to classify the reconstructed samples at the fixed bit depth and determine a filter based on the classification. The one or more processors are configured to adaptive loop filter the reconstructed samples at the input bit depth based on the determined filter and process the adaptively loop filtered samples.

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San Diego, CA, United States of America

Luong Pham Van