Qualcomm Incorporated

Vid Scale, Inc.

Louis Joseph Kerofsky

Marta Karczewicz

Geert Van Der Auwera

Bappaditya Ray

Adarsh Krishnan Ramasubramonian

Yan Ye

Liangping Ma

Gregory S Sternberg

Ralph Neff

Kiran Misra

Christopher A Segall

Yuwen He

Anantharaman Balasubramanian

Yuriy Reznik

Muhammed Zeyd Coban

Yong He

Dmytro Rusanovskyy

Luong Pham Van

Rahul Vanam

Alberto Rico Alvarino

Ghyslain Pelletier

Umesh Phuyal

Robert L Olesen

Ariela Zeira

Thomas Stockhammer

Min Wang

Samian Kaur

Dimitrios Karampatsis

Leena Zacharias

Nikolai Konrad Leung

Imed Bouazizi

Robert A DiFazio

Arash Vosoughi

Hilmi Enes Egilmez

Weimin Liu

Eduardo Asbun

Zhifeng Chen

Deepankar Katyal

Bing Han

Kuan-Ting Shen

Dangdang Shao

Khalid Tahboub

Michael Stefanick

Mikko Juhani Palatsi

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 device for decoding video data receives encoded video data encoded at a first bit-depth; receives in an encoded video bitstream for the video data one or more syntax elements; determines a dequantization shift and a mid-transform shift based on the one or more syntax elements; and performs an inverse transform of the encoded video data at a second bit-depth using the dequantization shift and the mid-transform shift to determine residual values for a block of video data, wherein the second bit-depth is different than the first bit-depth.

Reduced complexity transforms for high bitdepth 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

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

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.

Attribute residual coding in G-PCC

Various embodiments may provide methods, systems, and devices for supporting application level discovery of Radio Access Network (RAN) statistics and/or events. Various embodiments may provide methods, systems, and devices for supporting application level signaling of Quality of Service (QoS) requirements.

Interface between a radio access network and an application

An example method of encoding a point cloud includes determining one or more attribute parameters of an attribute of a point in the point cloud of a frame, wherein the one or more attribute parameters define how to determine or use a value of the attribute and are applicable to a plurality of points in the point cloud; and signaling, in a bitstream indicative of the point cloud, the one or more attribute parameters in a syntax structure that is specific to the frame.

Attribute parameter coding for geometry-based point cloud compression

An example device for decoding point cloud data includes: a memory configured to store point cloud data; and one or more processors implemented in circuitry and configured to: decode a frame of the point cloud data including a plurality of points, each of the points being associated with position values defining a respective position of the point; determine a global scaling factor for the frame; and scale the position values of each of the points by the global scaling factor. The scaling may be clipped to prevent the points exceeding the boundaries of a corresponding bounding box including respective points.

Global scaling for geometry-based point cloud coding

A method for decoding point cloud data comprises, based on a comparison of a maximum difference value and a threshold, applying an inverse function to a set of one or more jointly coded values to recover (i) residual values for attribute values of a current point of point cloud data and (ii) a predictor index that indicates a predictor in a predictor list, wherein predictors in the predictor list are based on attribute values of one or more neighbor points; determining predicted attribute values based on the predictor index; and reconstructing the attribute values of the current point based on the residual values and the predicted attribute values.

Predictor index signaling for predicting transform in geometry-based point cloud compression

A device comprises one or more processors configured to: obtain a value for a first laser, the value for the first laser indicating a number of probes in an azimuth direction of the first laser; decode a syntax element for a second laser, wherein the syntax element for the second laser indicates a difference between the value for the first laser and a value for the second laser, the value for the second laser indicating a number of probes in the azimuth direction of the second laser; determine the value for the second laser indicating the number of probes in the azimuth direction of the second laser based on the first value and the indication of the difference between the value for the first laser and the value for the second laser; and decode a point based on the number of probes in the azimuth direction of the second laser.

A G-PCC encoder and G-PCC decoder may quantize and scale, respectively, a position of a child node. The G-PCC encoder may control the precision of the quantization and scaling using a quantization parameter (QP) value and a parameter value k, wherein the parameter value k specifies a number of QP points per doubling of a scaling step size to be used at the G-PCC decoder.

Scale factor for quantization parameter values in geometry-based point cloud compression

A method of decoding video data includes determining an intra prediction mode from a plurality of intra prediction modes for a current block of the video data, determining a low frequency non-separable transform (LFNST) kernel from a plurality of LFNST kernels for the current block based on the determined intra prediction mode, wherein at least one LFNST kernel of the plurality of LFNST kernels is associated with at least two different intra prediction modes of the plurality of intra prediction modes, applying an inverse of the determined LFNST kernel to coefficient values generated from a transform unit (TU) of the current block to generate intermediate values, applying an inverse primary transform on the intermediate values to generate residual data, and reconstructing the current block based on the residual data.

Low frequency non-separable transform for video coding

A G-PCC coder is configured to receive the point cloud data, determine a final quantization parameter (QP) value for the point cloud data as a function of a node QP offset multiplied by a geometry QP multiplier, and code the point cloud data using the final QP value to create an coded point cloud.

Scaling of quantization parameter values in geometry-based point cloud compression (G-PCC)

In some examples, a method of decoding a point cloud includes decoding an initial QP value from an attribute parameter set. The method also includes determining a first QP value for a first component of an attribute of point cloud data from the initial QP value. The method further includes determining a QP offset value for a second component of the attribute of the point cloud data and determining a second QP value for the second component of the attribute from the first QP value and from the QP offset value. The method includes decoding the point cloud data based on the first QP value and further based on the second QP value.

Secondary component attribute coding for geometry-based point cloud compression (G-PCC)

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San Diego, CA, United States of America

Louis Joseph Kerofsky