Point Cloud Attribute Compression with Space-Filling Curve LOD
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Solution Overview
Problem
Point cloud data captured by sensors like LIDAR systems is large and costly to store and transmit, limiting its use in real-time applications due to high storage requirements and network resource consumption.
Innovation Solution
A system that compresses attribute information using an encoder, which assigns attribute values and determines attribute correction values based on neighboring points, and decompresses using a decoder that reconstructs attribute information using a hierarchical level of detail structure and space filling curves, enabling efficient storage and transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If point cloud data is stored and transmitted in full resolution, then data completeness and quality are maintained, but storage requirements and network resource consumption increase significantly
Solution Approach 1:
The point cloud data is divided into multiple levels of detail (LOD), where each level represents a different resolution. The encoder creates a hierarchical structure with coarse levels containing fewer points and fine levels containing more points. This segmentation allows the system to transmit only the necessary level based on application requirements, reducing storage and transmission needs while maintaining data completeness when all levels are preserved.
Solution Approach 2:
The patent implements a nested hierarchical structure where Level 0 (coarsest) is contained within Level 1, which is contained within Level 2, and so on. Each level embeds the previous level's data structure, allowing progressive refinement. This nesting enables efficient storage by reusing data across levels and allows flexible transmission by sending only the required detail level, resolving the contradiction between data completeness and storage requirements.
2Reliability
If point cloud data is transmitted in full resolution, then data quality is maintained, but network resource consumption and transmission time increase
Solution Approach 1:
The system dynamically selects which level of detail to transmit based on real-time network conditions, device capabilities, and application requirements. The encoder can adaptively choose to transmit only Level 0 for bandwidth-constrained scenarios or transmit multiple levels for high-quality requirements. This dynamic adaptation reduces network resource consumption while maintaining data quality when needed, resolving the contradiction between reliability and productivity.
3Productivity
If attribute information is compressed using prediction based on neighboring points, then compression efficiency increases, but computational complexity increases
Solution Approach 1:
The computational workload is segmented across multiple processing stages: spatial segmentation using space-filling curves to organize points, attribute segmentation by processing different attribute types separately, and level segmentation by applying prediction at different LOD levels. This segmentation reduces the complexity of each individual stage while maintaining overall compression efficiency through the systematic application of prediction across segmented data.
Solution Approach 2:
The patent uses copying of attribute values from neighboring points as the basis for prediction. Instead of complex calculations, the system copies attributes from spatially adjacent points (determined by space-filling curve ordering) and uses these copies as predictions. This simple copying approach achieves good compression efficiency while minimizing computational complexity, as copying is a basic operation compared to sophisticated prediction algorithms.
Data Source
AI summary
A system comprises an encoder configured to compress attribute information for a point cloud and/or a decoder configured to decompress compressed attribute information. Attribute values for at least one starting point are included in a compressed attribute information file and attribute correction values are included in the compressed attribute information file. An order for the points is determined based on a space filling curve, wherein an encoder and a decoder determine a same order for the points based on the space filling curve. Levels of detail are determined by sampling the ordered points according to different sampling parameters, and attribute values are predicted for the points in the levels of detail using the determined order. The encoder determines attribute correction values based on a comparison of the predicted values to an original value prior to compression. The decoder corrects the predicted attribute values based on received attribute correction values.