Point Cloud Occupancy Coding with Neighbour-Weighted Prediction
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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 in sparse populations, which hampers their adoption and deployment in applications like autonomous vehicles and virtual reality, due to challenges in coding geometry and spatial correlation.
Innovation Solution
The method involves context-adaptively coding occupancy data for child sub-volumes based on the occupancy status of neighboring sub-volumes, using weights that reflect the correlation between neighboring and child sub-volumes, and employing entropy encoding and decoding techniques to generate and reconstruct compressed bitstreams.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional compression methods are used for point cloud data, then implementation is simpler, but compression efficiency is insufficient especially for sparse point clouds
Solution Approach 1:
The point cloud data is divided into multiple sub-volumes (e.g., octree structure) to enable localized processing. Each sub-volume is encoded independently using occupancy prediction, allowing the system to handle sparse regions more efficiently while maintaining overall compression performance.
Solution Approach 2:
The patent performs preliminary occupancy prediction for child sub-volumes based on parent sub-volume occupancy status before actual encoding. This preliminary action allows the encoder to prepare probability models and prediction structures in advance, improving compression efficiency without significantly increasing runtime complexity.
2Measurement precision
If more data is stored to maintain fidelity, then accuracy is improved, but storage requirements increase
Solution Approach 1:
The patent employs feedback mechanisms where occupancy prediction results from parent sub-volumes are used to inform the encoding of child sub-volumes. This feedback loop allows the system to maintain high prediction accuracy by continuously refining probability models based on previously encoded data, reducing the need to store redundant information.
Solution Approach 2:
The system dynamically adjusts encoding parameters such as probability thresholds and prediction models based on the occupancy characteristics of different sub-volumes. By changing parameters adaptively rather than using fixed settings, the patent achieves high accuracy while optimizing storage requirements for different regions of the point cloud.
3Productivity
If compression is increased to reduce bandwidth, then transmission efficiency is improved, but decoding complexity increases
Solution Approach 1:
The patent introduces intermediate prediction structures and probability models that act as mediators between the compressed bitstream and the reconstructed point cloud. These intermediaries enable efficient decoding by providing structured guidance for reconstructing occupancy data without requiring complex computational operations at the decoder side.
Data Source
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AI summary
Methods and devices for encoding a point cloud, where occupancy data for child sub-volumes is context-adaptively encoded based on contexts selected, at least in part, using an occupancy score determined for each child sub-volume. The occupancy score for each child sub-volume of a parent sub-volume is determined based on the occupancy status of a plurality of neighbouring sub-volumes neighbouring the parent sub-volume and a respective weight assigned to each of the neighbouring sub-volumes vis-à-vis that child sub-volume, and wherein the respective weight reflects a strength of correlation between occupancy of that neighbouring sub-volume and occupancy of that child sub-volume.