Point Cloud Attribute Recomputing for Separate Geometry Compression
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Solution Overview
Problem
Existing deep learning-based point cloud compression methods excel in compressing geometry but struggle with effectively compressing associated attributes, leading to dissociation between geometry and attribute data.
Innovation Solution
A flexible configuration of deep learning-based frameworks that utilize an attribute recomputing process, such as a recoloring scheme, to generate attributes for the reconstructed point cloud, allowing separate use of geometry and attribute compression schemes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a single encoding process is used for both geometry data and attribute data, then the encoding process is simpler, but compression efficiency deteriorates because the encoding process cannot be optimized for each data type
Solution Approach 1:
The encoding process is segmented into separate first and second encoding processes. The first encoding process handles geometry data while the second encoding process handles attribute data. This segmentation allows each encoding process to be optimized for its specific data type, improving overall compression efficiency while maintaining clear functional boundaries between geometry and attribute processing
2Productivity
If different encoding processes are used for geometry data and attribute data, then compression efficiency improves, but the complexity of the encoding system increases
Solution Approach 1:
The encoding system is divided into distinct first and second encoding processes that operate independently on geometry and attribute data respectively. This segmentation reduces system complexity by creating modular, specialized components rather than requiring a single complex universal encoder, making the overall system more manageable and efficient
Solution Approach 2:
Each encoding process is tailored with local quality optimization - the first encoding process is specifically designed for geometry data characteristics while the second encoding process is optimized for attribute data characteristics. This localized optimization allows each component to achieve maximum efficiency for its specific function without compromising the other
3Productivity
If geometry data is encoded separately from attribute data, then compression gains improve, but correspondence between geometry and attribute data may be lost
Solution Approach 1:
While segmenting the encoding processes for optimization, the system maintains correspondence through coordinated operation of the first and second encoding processes. The segmentation enables independent optimization of each data type while the structured relationship between the two processes ensures that geometry and attribute data remain properly associated
Solution Approach 2:
The encoding system incorporates feedback mechanisms that monitor and maintain the correspondence between geometry data and attribute data throughout the separate encoding processes. This feedback ensures that while each data type is processed independently for maximum compression gains, the relationship and association between corresponding geometry and attribute points are preserved
Data Source
AI summary
A method of encoding point cloud data includes receiving, for a first encoding process, geometry data of the point cloud data of a source point cloud; encoding, in accordance with the first encoding process, the geometry data to generate encoded geometry data of a target point cloud and a geometry bitstream; decoding the encoded geometry data to generate reconstructed geometry data; performing an attribute recomputing process on attribute data of the point cloud data of the source point cloud based on the reconstructed geometry data to generate recomputed, reconstructed point cloud data of the target point cloud; and encoding, in accordance with a second encoding process, the recomputed, reconstructed point cloud data to generate an attribute bitstream.