Predictive Point Cloud Coding via Occupancy Patterns
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Solution Overview
Problem
Existing point cloud compression methods face inefficiencies due to the sparsely populated nature of point clouds, making it challenging to effectively encode and decode geometric data, particularly with predictive coding, where residual 3D vectors are costly to process.
Innovation Solution
The method involves predictive coding by determining a predicted set of points within a volumetric space, using a bit sequence to indicate occupancy patterns, and context-adaptively entropy coding based on selected context sets, which enables efficient encoding and decoding of point clouds by determining whether sub-volumes contain predicted points and adjusting context sets accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If predictive coding is used to encode point clouds, then compression efficiency is improved, but the cost of encoding and decoding residual 3D vectors increases
Solution Approach 1:
The patent segments the volumetric space into multiple sub-volumes and uses occupancy patterns to indicate which sub-volumes contain points. This segmentation allows the system to focus computational resources only on occupied sub-volumes rather than processing the entire volume, thereby reducing the cost of encoding and decoding residual vectors while maintaining compression efficiency through predictive coding.
Solution Approach 2:
The patent applies predictive coding selectively only to occupied sub-volumes identified through occupancy patterns, rather than applying it uniformly to the entire point cloud. This partial action approach reduces unnecessary computational overhead for empty regions while maintaining compression benefits where needed, resolving the contradiction between compression efficiency and processing cost.
2Measurement precision
If occupancy patterns are used to indicate sub-volume containment, then encoding accuracy is improved, but the number of contexts to track increases
Solution Approach 1:
The patent performs preliminary encoding of occupancy patterns to identify which sub-volumes contain points before applying predictive coding. This preliminary action establishes a foundation for accurate encoding by pre-identifying relevant regions, allowing the system to track fewer contexts specifically for occupied sub-volumes rather than maintaining contexts for the entire volumetric space, thus improving encoding accuracy while reducing contextual complexity.
Data Source
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AI summary
Methods and devices for encoding a point cloud. A bit sequence signaling an occupancy pattern for sub-volumes of a volume is coded. Predictive coding is used to find a set of predicted points, from which a corresponding predicted occupancy pattern may be determined. The predicted occupancy pattern may be used to determine the contexts for entropy coding the occupancy pattern. The determination may include determining for each sub-volume, whether it contains a predicted point or not and, in some cases, the count of predicted points within the sub-volume. Various threshold numbers of predicted points may cause the selection of different context sets for coding the occupancy pattern. The predictive coding may be enabled by determining that the prediction of occupancy of a parent volume to the volume met a threshold level of accuracy.