SGCC Point Cloud Compression via Geometry Sorting and Color Clustering
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Solution Overview
Problem
Existing point cloud compression methods fail to effectively compress large datasets of 3D points with color information, particularly when aiming for lossless geometry and lossy color coding, leading to inefficiencies and color degradation due to quantization errors.
Innovation Solution
The Sorted Geometry with Color Clustering (SGCC) method uses High Efficiency Video Coding (HEVC) to compress both geometry and color data, where geometry data is sorted to enhance correlation and color data is clustered to prevent color leakage, resulting in better compression efficiency compared to traditional approaches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional point cloud compression methods are used, then compression is achieved, but color degradation occurs due to quantization errors and geometry correlation is insufficient
Solution Approach 1:
The patent applies preliminary sorting to geometry data before compression to maximize spatial correlation, and preliminary clustering to group similar colors together before compression. This preliminary organization prevents color leakage during compression and ensures lossless geometry coding by establishing optimal data structures in advance.
Solution Approach 2:
The patent segments the point cloud data into separate geometry and color components, applying different compression strategies to each. Geometry data is sorted and compressed losslessly, while color data is clustered and compressed with controlled quantization, preventing color degradation while maintaining geometry precision.
2Productivity
If lossy compression is applied to both geometry and color, then compression ratio improves, but geometry precision deteriorates
Solution Approach 1:
The patent applies different quality levels to different data components: lossless compression is applied to geometry data to preserve precision, while lossy compression is applied to color data to improve compression efficiency. This local differentiation of quality requirements resolves the contradiction between compression efficiency and geometry precision.
3Device complexity
If geometry data is not sorted, then processing is simpler, but compression efficiency deteriorates due to poor correlation
Solution Approach 1:
The patent performs preliminary sorting of geometry data along the x, y, and z axes before compression to maximize spatial correlation. This preliminary action improves compression efficiency by creating highly correlated data sequences, while the sorting algorithm is designed to be computationally efficient, balancing the trade-off between processing complexity and compression performance.
Data Source
AI summary
A point cloud compression approach that exploits HEVC for compression of both the geometry and color data is described herein. Although both the geometry and color could be compressed in a lossy fashion, the point cloud compression approach is suggested for the scenario of lossless geometry and lossy color coding. Both geometry and color data are first mapped into 2D images and then compressed by HEVC. A sorting technique is used to sort the geometry data to make it as correlated as possible when it is mapped to a 2D image. For color coding, clustering is used to put similar colors in a point cloud into spatial neighbors in the mapped 2D image. This significantly avoids color leaking due to quantization errors to neighbor points in 3D. The results show that much better compression is achieved compared to the Anchor when it is configured for lossless geometry coding.