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

Xiang Li

Yunfei Zheng

Amir Said

Hongtao Wang

Kai Wang

Sanjeev Kumar

Yang Yu

Yekui Wang

Shu Xiao

Siping Tao

Bing Han

Hendry

Kuan-Ting Shen

Dangdang Shao

Khalid Tahboub

Ying Chen

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

A device for decoding a bitstream that includes point cloud data can be configured to determine, based on syntax signaled in the bitstream, that in-tree quantization is enabled for a node; determine, for the node based on the syntax signaled in the bitstream, that an angular mode is activated for the node; in response to in-tree quantization being enabled for the node, determine for the node a quantized value representing a coordinate position relative to an origin position; scale the quantized value without clipping to determine a scaled value representing the coordinate position relative to the origin position; and determine a context for context decoding a plane position syntax element for the angular mode based on the scaled value representing the coordinate position relative to the origin position.

Angular mode and in-tree quantization in geometry 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

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

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

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Del Mar, CA, United States of America

Geert Van Der Auwera