Point Cloud Occupancy Coding Using Rate-Distortion Bit Inversion
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Solution Overview
Problem
Current methods for compressing point cloud data are inefficient, particularly due to the sparse population of point clouds, which complicates the coding of geometry, and existing tree-based structures do not effectively manage bitrate and distortion in lossy coding.
Innovation Solution
The method involves recursive splitting of a volumetric space into sub-volumes, determining a rate-distortion cost for inverting occupancy bits at maximum depth, and applying entropy encoding to optimize bitrate and distortion, allowing for lossy encoding by adding or removing points, thereby introducing controlled distortion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If lossless tree-based coding is used for occupancy data, then coding accuracy is maintained, but compression efficiency is insufficient
Solution Approach 1:
The patent changes the coding parameter from lossless to lossy coding, allowing controlled distortion in occupancy data representation. This enables significant compression gains by accepting approximate representations rather than exact binary occupancy flags, directly resolving the contradiction between maintaining coding accuracy and improving compression efficiency.
2Shape
If tree-based coding structure is used, then spatial organization is achieved, but bitrate management is inefficient
Solution Approach 1:
The patent modifies the bitrate parameter by introducing lossy coding mechanisms that reduce the number of bits required to represent occupancy data. By allowing controlled approximation rather than exact representation, the system achieves better bitrate management while preserving the hierarchical tree-based spatial organization structure.
3Measurement precision
If maximum depth recursive splitting is applied, then spatial resolution is improved, but distortion increases
Solution Approach 1:
The patent applies partial splitting by not always reaching maximum depth for all sub-volumes. Instead, it performs adaptive splitting where recursive division is applied only where necessary to achieve acceptable spatial resolution, accepting some approximation in less critical regions. This balances spatial resolution improvement with distortion control.
4Productivity
If lossy encoding is implemented, then compression performance improves, but coding complexity increases
Solution Approach 1:
The patent manages coding complexity by systematically changing from lossless to lossy coding parameters. This transition, while improving compression performance, requires implementing rate-distortion optimization algorithms and distortion metrics, thereby increasing coding complexity. The principle is applied to achieve the desired compression performance improvement.
Data Source
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.