Qualcomm Incorporated

Shumaker & Sieffert, P.a.

Geert Van Der Auwera

Marta Karczewicz

Adarsh Krishnan Ramasubramonian

Bappaditya Ray

Luong Pham Van

Muhammed Zeyd Coban

Louis Joseph Kerofsky

Vadim Seregin

Xianglin Wang

In Suk Chong

Wei-Jung Chien

Fnu Hendry

Han Huang

Cheng-Teh Hsieh

Rajan Laxman Joshi

Yu Han

Thibaud Laurent Biatek

Ye-Kui Wang

Alican Nalci

Dmytro Rusanovskyy

Nikolai Konrad Leung

Hilmi Enes Egilmez

Yung-Hsuan Chao

Chengjie Tu

Li Feng Zhang

Hongbin Liu

Ying Hai Chen

Yunfei Zheng

Amir Said

Hongtao Wang

Sanjeev Kumar

Kai Wang

Yekui Wang

Shu Xiao

Siping Tao

Hendry

Bing Han

Kuan-Ting Shen

Dangdang Shao

Khalid Tahboub

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

Certain aspects of the present disclosure provide techniques for global motion modeling. Embodiments include receiving a first image and a second image from a camera attached to a moving object. Embodiments include identifying a pixel in the first image. Embodiments include determining, based on one or more parameters associated with the camera, a vector representing a range of locations in which a real-world point corresponding to the pixel is likely to be found in the second image, wherein the parameters associated with the camera comprise: a first parameter related to a location of the camera relative to a ground surface; a second parameter related to motion of the moving object; and a third parameter related to an orientation of the camera relative to the ground surface. Embodiments include determining, using the vector, a location of the real-world point in the second image.

Global motion modeling for automotive image data

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.

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

Techniques are described for decoding video data. A video decoder may determine chroma blocks in a chroma quantization group (QG) of the video data, determine a quantization parameter predictor that is the same for each of the chroma blocks of the chroma QG, determine an offset value that is the same for two or more of the chroma blocks of the chroma QG, determine a quantization parameter value for each of the two or more of the chroma blocks in the chroma QG based on the quantization parameter predictor and the offset value inverse quantize coefficients of one or more residual blocks for the chroma blocks based on the determined quantization parameter value, generate the one or more residual blocks based on the inverse quantized coefficients, and reconstruct the chroma blocks based on the one or more residual blocks.

Chroma quantization parameter (QP) derivation for video coding

An example method of encoding a point cloud includes determining that residual values for all components except one component of an attribute of a point in the point cloud are equal to zero; based on the determination that the residual values for all components except the one component of the attribute are equal to zero, determining a value for the one component that is equal to a magnitude of a residual value of the one component of the attribute minus an offset; encoding the value of the one component; and signaling the encoded value in a bitstream.

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Del Mar, CA, United States of America

Geert Van Der Auwera