Adaptive Point Cloud Attribute Coding for LoD Generation
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Solution Overview
Problem
Current graph-based point cloud compression (G-PCC) methods ignore local point distribution when generating levels of detail (LoD), leading to inefficient compression and data representation, particularly in applications requiring real-time communication and dynamic mapping.
Innovation Solution
Adaptive point cloud attribute coding method that determines a centroid of candidate points and selects points closest to it for lower LoD layers, while excluding them from upper layers, to account for local point statistics and optimize LoD layer generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If fixed-rate regular sampling is used to generate LoD layers, then the method is simple and consistent, but it ignores local point distribution and produces suboptimal compression results
Solution Approach 1:
The patent applies local quality by transitioning from uniform fixed-rate sampling to adaptive sampling that responds to local point distribution characteristics. The encoder dynamically adjusts sampling rates based on local density metrics, allowing different regions of the point cloud to be sampled at appropriate rates rather than applying a single global sampling rate to all regions.
Solution Approach 2:
The patent implements dynamics by making the sampling rate adaptive rather than static. The sampling rate varies dynamically based on local point density conditions, allowing the system to optimize compression performance for different regions of the point cloud while maintaining a systematic approach to LoD generation.
2Productivity
If adaptive sampling based on local point distribution is used, then compression efficiency is improved, but the complexity of LoD generation increases
Solution Approach 1:
The patent applies parameter changes by modifying the sampling rate parameter based on local point density conditions. The system calculates local density metrics and uses these to adjust sampling parameters adaptively, allowing optimization of compression efficiency through parameter adaptation rather than through complex structural changes to the encoding framework.
Data Source
AI summary
A method of adaptive point cloud attribute coding is performed by at least one processor and includes determining a centroid of k candidate points of a point cloud, k being a predetermined sampling rate of all points of the point cloud, and selecting one of the k candidate points that is closest to the centroid. The method further includes determining a first levels-of-detail (LoD) layer comprising the one of the k candidate points, and a second LoD layer without the one of the k candidate points, and performing coding of attributes of the all points, based on the first LoD layer and the second LoD layer.