The present disclosure relates to an image processing apparatus and method that can prevent an increase in the load of a decoding process for encoded data in a point cloud video-based approach. The parameters related to a plurality of point cloud models of a point cloud are transformed, a two-dimensional plane image onto which the plurality of point cloud models having the transformed parameters is projected is encoded, and a bitstream containing encoded data of the two-dimensional image and transform information that is information regarding the transform of the parameters is generated. The present disclosure can be applied to an information processing device, an image processing apparatus, an electronic apparatus, an information processing method, a program, or the like, for example.

The present disclosure relates to an image processing apparatus and method that can prevent an increase in the load of a decoding process for encoded data in a point cloud video-based approach. The parameters related to a plurality of point cloud models of a point cloud are transformed, a two-dimensional plane image onto which the plurality of point cloud models having the transformed parameters is projected is encoded, and a bitstream containing encoded data of the two-dimensional image and transform information that is information regarding the transform of the parameters is generated. The present disclosure can be applied to an information processing device, an image processing apparatus, an electronic apparatus, an information processing method, a program, or the like, for example.

A search space for performing nearest neighbor searches for encoding point cloud data may be trimmed. Ranges of a space filling curve may be used to identify search space to exclude or reuse, instead of generating nearest neighbor search results for at least some of the points of a point cloud located within some of the ranges of the space filling curve. Additionally, neighboring voxels may be searched to identify any neighboring points missed during the trimmed search based on the ranges of the space filling curve.

A real-time pose measurement method of a planar coding target for a vision system. The planar coding target includes a plurality of coding elements, a coding block, a coding template, a minimum identification unit pattern and a coding pattern. Each coding element has a unique coding value, and serial numbers of the coding elements are different from each other. The coding block includes four coding elements that are distributed in the same rectangle ABCD and do not overlap with each other. A center of the coding block is an intersection point O of two diagonals of the rectangle ABCD. A coding value of the coding block is associated with coding values of the four coding elements contained therein.

Disclosed herein is a method for compressing point cloud data. The method includes quantizing input point cloud data, generating a global motion matrix based on the quantized point cloud data, applying the global motion matrix based on whether local motion compression is performed, and compressing data to which the global motion matrix is applied.

Disclosed are an image processing method and device using a line-wise operation. The image processing device, according to one embodiment, comprises: a receiver for receiving an image; a first convolution operator for generating a feature map by performing a convolution operation on the basis of the image; and a compressor for compressing the feature map into units of at least one line; and a decompressor for reconstructing the feature map compressed into units of lines.

Disclosed are an image processing method and device using a line-wise operation. The image processing device, according to one embodiment, comprises: a receiver for receiving an image; a first convolution operator for generating a feature map by performing a convolution operation on the basis of the image; and a compressor for compressing the feature map into units of at least one line; and a decompressor for reconstructing the feature map compressed into units of lines.

Methods and tiling engines for tiling primitives in a tile based graphics processing system in which a rendering space is divided into a plurality of tiles. The method includes generating a multi-level hierarchy of tile groups, each level of the multi-level hierarchy comprising one or more tile groups comprising one or more of the plurality of tiles; receiving a plurality of primitive blocks, each primitive block comprising geometry data for one or more primitives; associating each of the plurality of primitive blocks with one or more of the tile groups up to a maximum number of tile groups such that if at least one primitive of a primitive block falls, at least partially, within the bounds of a tile, the primitive block is associated with at least one tile group that includes that tile; and generating a control stream for each tile group based on the associations, wherein each control stream comprises a primitive block entry for each primitive block associated with the corresponding tile group.

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.

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.