3D Point Cloud Compression With Shape-Adaptive Transforms
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Solution Overview
Problem
Existing methods for compressing 3D point clouds, which often contain 'holes' or unoccupied positions, are inefficient as they are based on block-based techniques designed for images and videos where all positions are occupied, leading to difficulties in applying prediction and transforms effectively.
Innovation Solution
A method for compressing 3D point clouds by partitioning them into blocks, using projections and modified shape-adaptive transforms that accommodate 'holes' or unoccupied positions, and employing preprocessing to arrange points on a uniform grid, followed by intra prediction and either 3D shape-adaptive DCT or graph transforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If block-based compression methods designed for images and videos are applied to 3D point clouds, then compression efficiency can be improved, but the methods fail to handle unoccupied positions or 'holes' in the point cloud blocks
Solution Approach 1:
The patent applies different processing strategies to different regions within a block based on occupancy status. Occupied positions undergo prediction and transform operations, while unoccupied positions are handled separately through occupancy mapping. This local differentiation allows the system to maintain compression efficiency for occupied regions while properly representing the sparse structure of point clouds.
Solution Approach 2:
The patent segments the block processing into two distinct components: occupancy information (which positions are filled) and attribute information (values at occupied positions). This segmentation allows the use of traditional block-based compression for attributes while separately encoding occupancy patterns, thereby resolving the contradiction between compression efficiency and handling unoccupied positions.
2Loss of energy
If traditional prediction and transform operations are applied to all positions in a block, then compression can be achieved, but computational resources are wasted on unoccupied positions
Solution Approach 1:
The patent applies prediction and transform operations only to occupied positions rather than all positions in the block. By performing partial action only where needed (at occupied positions), the system avoids wasting computational resources on unoccupied positions while maintaining compression performance through selective processing.
3Reliability
If the point cloud is represented with high detail to maintain quality, then the data size increases significantly, but compression becomes more difficult
Solution Approach 1:
The patent transforms the point cloud data from spatial coordinates to frequency domain representations through transform operations (such as DCT or wavelet transforms). This parameter change allows the data to be compressed by concentrating energy in fewer coefficients, thereby reducing data size while maintaining reconstruction quality through selective coefficient encoding.
Data Source
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AI summary
A method compresses a point cloud composed of a plurality of points in a three-dimensional (3D) space by first acquiring the point cloud with a sensor, wherein each point is associated with a 3D coordinate and at least one attribute. The point cloud is partitioned into an array of 3D blocks of elements, wherein some of the elements in the 3D blocks have missing points. For each 3D block, attribute values for the 3D block are predicted based on the attribute values of neighboring 3D blocks, resulting in a 3D residual block. A 3D transform is applied to each 3D residual block using locations of occupied elements to produce transform coefficients, wherein the transform coefficients have a magnitude and sign. The transform coefficients are entropy encoded according the magnitudes and sign bits to produce a bitstream.