Point Cloud Attribute Compression with Geometry-Decoupled Feature Coding
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Solution Overview
Problem
Existing learning-based point cloud compression methods struggle to effectively extract representative point cloud attribute features, leading to inefficient compression due to the inclusion of geometry information in attribute features, and suffer from inaccurate probability estimation and high complexity in octree-based attribute coding.
Innovation Solution
A method for point cloud attribute compression using a bottom-up strategy that estimates attribute probability distribution from finer octree levels, incorporating a feature aggregator and attribute probability estimator to generate more representative features for hierarchical lossless and lossy coding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If geometry information is included in attribute features, then feature representation is more comprehensive, but compression efficiency deteriorates due to redundancy
Solution Approach 1:
The patent segments the point cloud processing into two independent streams: geometry processing and attribute processing. The geometry stream extracts spatial structure information, while the attribute stream processes color and reflectance information separately. This segmentation removes the redundancy of geometry information being included in attribute features, improving compression efficiency while maintaining representation accuracy through the dedicated geometry stream.
Solution Approach 2:
The patent extracts and removes geometry information from the attribute feature representation. By using a geometry-aware attribute encoder that takes geometry as a separate input condition rather than embedding it within attribute features, the method extracts only the necessary attribute information while leveraging geometry context separately, thus improving compression efficiency without sacrificing representation quality.
2Device complexity
If octree-based attribute coding is used, then hierarchical structure is achieved, but complexity increases due to probability estimation requirements
Solution Approach 1:
The patent performs preliminary action by pre-processing the point cloud to extract geometry features and organizing the data into an octree structure before attribute encoding. The geometry-aware attribute encoder pre-computes geometry-based context information that is then used during attribute coding, eliminating the need for complex probability estimation during the actual coding process and reducing overall system complexity.
3Measurement precision
If finer octree levels are used for feature extraction, then probability estimation accuracy is improved, but processing complexity increases
Solution Approach 1:
The patent applies local quality by using finer octree levels selectively for feature extraction only where needed, rather than processing the entire point cloud at the finest resolution. The geometry-aware attribute encoder uses multi-scale feature extraction, combining information from different octree levels, thereby achieving high probability estimation accuracy while reducing overall processing complexity through selective fine-level processing.
Data Source
AI summary
In one implementation, a method for reconstructing attributes of a point cloud is provided wherein a geometry feature map representative of a geometry of the point cloud is obtained, an attribute feature map is decoded from a bitstream, the attribute feature map being representative of attributes of points of the point cloud, and the decoded attribute feature map is updated with the geometry feature map. Attributes of points of the point cloud are reconstructed based on the updated decoded attribute feature map.