Qualcomm Incorporated

Shumaker & Sieffert, P.a.

Adarsh Krishnan Ramasubramonian

Marta Karczewicz

Ye-Kui Wang

Geert Van Der Auwera

Fnu Hendry

Bappaditya Ray

Dmytro Rusanovskyy

Ying Hai Chen

Luong Pham Van

Vadim Seregin

Joel Sole Rojals

Done Bugdayci Sansli

Louis Joseph Kerofsky

Muhammed Zeyd Coban

Sungwon Lee

Krishnakanth Rapaka

Ying Chen

Jianle Chen

Cheng-Teh Hsieh

Wei-Jung Chien

Thibaud Laurent Biatek

Xiang Yang Li

Han Huang

Li Feng Zhang

Rajan Laxman Joshi

Yong He

Yi-Wen Chen

Nan Hu

Hilmi Enes Egilmez

Yao-Jen Chang

Yan Zhang

Yu Han

Yekui Wang

A method of encoding a point cloud includes determining, by one or more processors, a quantity of lasers used to capture light detection and ranging (LIDAR) data that represents the point cloud; and encoding, by the one or more processors, a laser index for a current node of the point cloud, wherein encoding the laser index comprises: obtaining a predicted laser index value of the current node; determining a residual laser index value for the current node, wherein determining the residual laser index value comprises constraining a sum of the residual laser index value and the predicted laser index value to be less than or equal to the determined quantity of lasers minus one; and encoding, in a bitstream, one or more syntax elements that represent the residual laser index value.

Clipping laser indices in predictive geometry coding for point cloud compression

An example device includes memory configured to store the point cloud data and one or more processors configured to determine a first point of the point cloud data to be a first node of a first prediction tree branch. The one or more processors are configured to determine that a first azimuth difference between the first point and a second point of the point cloud data does not meet a first azimuth threshold, and based on that determination, determine the second point to be a second node of the first prediction tree branch. The one or more processors are configured to determine that a second azimuth difference between a third point of the point cloud data and a fourth point of the point cloud data meets the first azimuth threshold and based on that determination, determine the fourth point to be a first node of a second prediction tree branch.

Predictive geometry coding of point cloud

A method of processing a point cloud includes determining that angular mode is enabled for encoding or decoding a current point of points in the point cloud, and parsing or signaling information for an azimuthal angle residual value for the current point independent of a radius value of the current point or a radius value of a previous point of the point cloud that is previous to the current point in decoding order. The azimuthal angle residual value is based on a difference between an azimuthal angle value of the current point and a predictor azimuthal angle value of the current point, and the azimuthal angle value of the current point is indicative an azimuthal angle of the current point based on a laser used to capture the points of the point cloud.

Predictive geometry coding for point cloud compression

A method of encoding point cloud data includes determining an amount by which a laser turns for determining points in a point cloud represented by the point cloud data, generating a syntax element indicative of the amount by which the laser turns, wherein a value of the syntax element is a defined value less than the amount by which the laser turns, and signaling the syntax element.

High level syntax refinements for geometry point cloud compression (G-PCC)

A method of encoding point cloud data comprises storing, in a buffer, a maximum coordinate of a pair of coordinates of an applicable node, wherein the applicable node is a most-recently encoded node with a same position as a current node along an applicable axis and the pair of coordinates are for axes different from the applicable axis; determining a context for a planar mode plane position of the current node, wherein determining the context for the planar mode plane position comprises determining, based on the maximum coordinate of the pair of coordinates of the applicable node, a distance value representing a distance between the current node and the applicable node; determining an increment value that indicates whether the distance value is greater than a threshold; and determining the context index based on the increment value; and entropy encoding the planar mode plane position using the determined context.

GPCC planar mode and buffer simplification

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 comprises obtaining a first laser angle; obtaining a second laser angle; obtaining a laser angle difference for a third laser angle; determining a predicted value based on the first laser angle and the second laser angle; and determining the third laser angle based on the predicted value and the laser angle difference for the third laser angle.

Coding of laser angles for angular and azimuthal modes in geometry-based point cloud compression

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 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.

Inter prediction coding for geometry point cloud compression

A device for encoding point cloud data, the device comprising: a memory to store the point cloud data; and one or more processors coupled to the memory and implemented in circuitry, the one or more processors configured to: determine a horizontal plane position of a node, wherein the horizontal plane position indicates a position of a single plane that is perpendicular to a first axis of a coordinate system, wherein the first axis is a horizontal axis; determine, from a plurality of contexts consisting of 8 contexts, a context for the horizontal plane position of the node; and perform arithmetic encoding on a syntax element indicating the horizontal plane position using the determined context.

Planar and azimuthal mode in geometric 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 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.

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San Diego, CA, United States of America

Adarsh Krishnan Ramasubramonian