Point Cloud Occupancy Coding with Bit Inversion and Rate-Distortion
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Solution Overview
Problem
Current point cloud compression methods are inefficient in encoding and decoding large datasets of three-dimensional objects, particularly due to the sparse population of point clouds, which complicates the coding of geometry and results in suboptimal data transmission and storage.
Innovation Solution
The proposed method involves inverting occupancy bits in a tree-based structure to introduce controlled distortion at the maximum depth level of the octree, allowing for lossy coding by adding or removing points, and using rate-distortion optimization to determine the most cost-effective bit inversion sequences for entropy encoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If lossless coding is used for occupancy bits in tree-based structure, then measurement precision is maintained, but data compression efficiency deteriorates
Solution Approach 1:
The patent applies parameter changes by transitioning from lossless to lossy coding of occupancy bits. Specifically, it introduces a distortion threshold parameter and allows occupancy bits to be inverted when the resulting distortion remains below this threshold. This parameter-based approach enables the system to achieve better compression ratios while maintaining acceptable reconstruction accuracy, directly resolving the contradiction between measurement precision and compression efficiency.
Solution Approach 2:
The patent converts the harmful effect of occupancy bit inversion (which introduces distortion) into a beneficial outcome for compression. By deliberately allowing controlled inversions of occupancy bits when they result in lower overall distortion after point cloud reconstruction, the system transforms what would normally be considered data degradation into a mechanism for achieving superior compression performance while maintaining acceptable quality.
2Quantity of substance
If point cloud data is sparsely populated, then data storage requirements are reduced, but coding complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the point cloud data into multiple octree levels and processing occupancy bits at different depths separately. The method segments the coding process into stages: first coding higher-level occupancy bits, then progressively coding lower-level bits with reference to previously coded levels. This segmentation reduces coding complexity by breaking down the challenging task of compressing sparse point cloud data into more manageable sub-tasks that can be processed efficiently.
3Productivity
If occupancy bits are inverted to achieve lossy coding, then compression ratio is improved, but distortion increases
Solution Approach 1:
The patent implements feedback mechanisms through rate-distortion optimization processes that evaluate the impact of occupancy bit inversions. The system calculates distortion metrics for potential inversions and uses this feedback to determine whether to invert specific bits. Additionally, the decoder uses side information about inverted bits to compensate for distortions, creating a feedback loop that allows the system to achieve better compression ratios while minimizing information loss through intelligent compensation strategies.
Data Source
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AI summary
Methods and devices for lossy encoding of point clouds. Rate-distortion optimization is used in coding an occupancy pattern for a sub-volume to determine whether to invert any of the bits of the occupancy pattern. The assessment may be a greedy evaluation of whether to invert bits in the coding order. Inverting a bit of the occupancy pattern amounts to adding or removing a point from the point cloud. A distortion metric may measure distance between the point added or removed and its nearest neighbouring point.