Point Cloud Attribute Decoding With Geometry-Based Predictors
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Solution Overview
Problem
Existing point cloud compression techniques face inferior prediction and compression quality due to indiscriminate replacement of predictor candidates, particularly in specific coding scenarios.
Innovation Solution
A G-PCC coding device determines predictor candidates based on a comparison of the locations of the second and third closest already-decoded points to the current point, preventing replacement where it decreases prediction quality while allowing it where it increases quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the replacement process is applied to all coding scenarios, then compression efficiency is improved in general cases, but prediction quality deteriorates in specific scenarios
Solution Approach 1:
The patent applies different candidate replacement strategies to different coding scenarios based on local characteristics. Specifically, it evaluates the geometric relationship between the current point and its neighbors, and only performs replacement when the relationship indicates potential benefit (e.g., when neighbors are collinear or coplanar with the current point). This localized approach prevents degradation in scenarios where replacement would be harmful while maintaining improvements in scenarios where it is beneficial.
2Ease of operation
If predictor candidates are replaced based only on distance to current point, then nearest neighbors are selected, but prediction accuracy decreases when farther points provide better prediction
Solution Approach 1:
The patent changes the selection parameters for predictor candidates from purely distance-based to geometry-based. It evaluates whether the current point and its neighbors satisfy specific geometric relationships (collinearity, coplanarity) and uses these geometric parameters to determine candidate replacement. This allows the system to select farther points as candidates when geometric relationships indicate they would provide better prediction accuracy.
3Device complexity
If replacement is performed without geometric analysis, then processing is simpler, but prediction quality deteriorates in scenarios with specific spatial relationships
Solution Approach 1:
The patent performs preliminary geometric analysis of the spatial relationships between the current point and its nearest neighbors before deciding whether to perform candidate replacement. It checks if the points satisfy specific geometric relationships (collinear, coplanar) in advance, and only proceeds with replacement when the analysis indicates potential benefit. This preliminary action prevents unnecessary complexity in scenarios where replacement would not improve prediction quality.
Data Source
AI summary
A device for processing point cloud data is configured to determine a first attribute value for a first point of a point cloud, the first point being a closest already-decoded point to a current point of the point cloud; determine second and third attribute values for second and third points of the point cloud, the second and third points being second and third closest already-decoded points; determine a fourth attribute value for a fourth point of the point cloud, the fourth point being an already-decoded point that is either further from, or the same distance to, the current point as the third point; generate a set of predictor candidates with a subset of the first attribute value, the second attribute value, the third attribute value, and the fourth attribute value based on a comparison of a location of the second point to a location of the third point.