Point Cloud Decoding with Selective Attribute Smoothing
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Solution Overview
Problem
The existing technologies for compressing and decompressing point clouds often require specialized hardware and result in visual artifacts due to the conversion of 3D point clouds into 2D frames, which affects the quality of the reconstructed point cloud.
Innovation Solution
A method and apparatus for point cloud decoding that includes a processor configured to decode a bitstream into frames, organize pixels into patches corresponding to clusters of points, and reconstruct the 3D point cloud, with the option to perform smoothing based on frame properties to improve visual quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If point clouds are compressed by converting to 2D frames, then bandwidth requirement is reduced, but visual quality deteriorates due to artifacts
Solution Approach 1:
The patent applies dimensionality change by converting 3D point cloud data into 2D frame representations for compression, then reconstructing the 3D structure. This allows standard 2D compression algorithms to be used while recovering 3D visual quality through depth information and attribute smoothing operations that mitigate artifacts introduced during the dimensionality reduction and reconstruction process.
2Productivity
If specialized hardware is used for point cloud compression, then compression efficiency is improved, but device complexity increases
Solution Approach 1:
The patent employs universal 2D video compression algorithms that can be implemented on standard decoding devices without specialized hardware. By mapping point cloud data to 2D frames and using conventional compression techniques, the system achieves efficient compression while maintaining compatibility with general-purpose devices, eliminating the need for dedicated point cloud compression hardware.
3Manufacturing precision
If smoothing is always performed on reconstructed point cloud, then visual quality is improved, but processing time increases
Solution Approach 1:
The patent implements selective smoothing that applies processing only to specific regions of the reconstructed point cloud where artifacts are most prominent, such as boundary areas between patches. By targeting only these local regions rather than applying uniform smoothing across the entire point cloud, the system improves visual quality in critical areas while minimizing overall processing time and computational overhead.
Data Source
AI summary
A method for point cloud decoding includes receiving a bitstream. The method also includes decoding the bitstream into multiple frames that include pixels. A portion of the pixels are organized into patches and correspond to respective clusters of points of a 3D point cloud. The method further includes decoding, from the bitstream, an occupancy map frame. The occupancy map frame indicates the portion of the pixels included in the multiple frames that represent the points of the 3D point cloud. In addition, the method includes reconstructing the 3D point cloud using the multiple frames and the occupancy map frame. The method also includes determining whether to perform smoothing to the 3D point cloud based at least in part on properties of the multiple frames. Based on determining to perform the smoothing, the method includes performing the smoothing to the 3D point cloud.