A method of encoding point cloud data using a planar coding mode is disclosed. The planar coding mode may be signaled using a planar mode flag to signal that a current volume is planar. A volume is planar if all of its occupied child nodes are on one side of a plane bisecting the volume. A planar position flag may signal which side of the volume is occupied. Volume data for already-coded occupied volumes of the point cloud is tracked using a data structure stored in memory. Entropy coding may be used to code the planar mode flag and/or the planar position flag. Context determination for coding may take into account a distance between the volume and a closest already-coded occupied volume among those tracked already-coded occupied volumes that have a same index in the data structure as the current volume.

Technologies for providing shared memory for accelerator sleds includes an accelerator sled to receive, with a memory controller, a memory access request from an accelerator device to access a region of memory. The request is to identify the region of memory with a logical address. Additionally, the accelerator sled is to determine from a map of logical addresses and associated physical address, the physical address associated with the region of memory. In addition, the accelerator sled is to route the memory access request to a memory device associated with the determined physical address.

Techniques related to graphics rendering including techniques for compression and/or decompression of graphics data by use of indexed subsets are described.

Methods, apparatus, systems and articles of manufacture to identify a video decoding error are disclosed. An example apparatus includes an atlas generator to generate atlas data for one or more atlases generated from input views of video; a hash generator to: perform a hash operation on the atlas data to generate a hash value; and include the hash value in a message; and a multiplexer to combine the one or more atlases, coded atlas data corresponding to the atlas data, and the message to generate a video bitstream.

An image decoding apparatus obtain pieces of coded data that is included in a bitstream and generated by coding tiles. Tile boundary independence information is further obtained from the bitstream, with the tile boundary independence information indicating whether each of boundaries between the tiles is one of a first boundary or a second boundary. The pieces of coded data are decoded to generate image data of the tiles. Image data of a first tile is generated by decoding a first code string included in first coded data with reference to decoding information of a decoded tile when the tile boundary independence information indicates the first boundary, and by decoding the first code string without referring to the decoding information of the decoded tile when the tile boundary independence information indicates the second boundary.

In a 3D point cloud compression system based on multi-scale structured dictionary learning, a point cloud data partition module outputs a voxel set and a set of blocks of voxels of different scales. A geometric information encoding module outputs an encoded geometric information bit stream. A geometric information decoding module outputs decoded geometric information. An attribute signal encoding module outputs a sparse coding coefficient matrix and a learned multi-scale structured dictionary. An attribute signal compression module outputs a compressed attribute signal bit stream. An attribute signal decoding module outputs decoded attribute signals. A 3D point cloud reconstruction module completes reconstruction. The system is applicable to lossless geometric and lossy attribute compression of point cloud signals. Based on the natural hierarchical partitioning structure of point cloud signals, the system gradually improves the reconstruction quality of high-frequency details in the signals from coarse scale to fine scale, and achieves significant gains.

A method, computer system, and computer-readable medium are provided for point cloud attribute coding by at least one processor. Data associated with a point cloud is received. The received data is transformed through a lifting decomposition based on enabling a scalable coding of attributes associated with the lifting decomposition. The point cloud is reconstructed based on the transformed data.

A decoding method executed by a decoding device for decoding encoded data from point cloud data includes: acquiring an occupancy code represented in an N (1≤N≤8)-ary tree structure by decoding the encoded data; repeating a process of generating an N-ary tree structure until a block has a predetermined size, the process being a process in which a cube configured to include all the point cloud data is divided into eight blocks and thereafter when a bit of an occupancy code corresponding to a divided block is 1, the block is further divided; and acquiring, as coordinates of a point in the point cloud data, coordinates of a block of the predetermined size where a bit of an occupancy code corresponding to the block of the predetermined size is 1. In the repeating, the decoding device determines a value of N of the N-ary tree structure for a block to be divided on a basis of an inclusion relation between the block to be divided and a non-occupancy region set in advance.

Systems and methods are disclosed for fused processing of a continuous mathematical operator. Fused processing of continuous mathematical operations, such as pointwise non-linear functions without storing intermediate results to memory improves performance when the memory bus bandwidth is limited. In an embodiment, a continuous mathematical operation including at least two of convolution, upsampling, pointwise non-linear function, and downsampling is executed to process input data and generate alias-free output data. In an embodiment, the input data is spatially tiled for processing in parallel such that the intermediate results generated during processing of the input data for each tile may be stored in a shared memory within the processor. Storing the intermediate data in the shared memory improves performance compared with storing the intermediate data to the external memory and loading the intermediate data from the external memory.

Examples relate to image processing, and performing fixed length format cell compression on a cell of a planar colour image based on an amount of white colour of the cell, the cell comprising a plurality of pixels, to obtain a compressed cell having four or fewer colour levels; and performing variable length format cell compression on the compressed cell to obtain a coded compressed cell.