Video Point Cloud Recoloring via KD-Tree Neighbor Averaging
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Solution Overview
Problem
Current video-based point cloud compression technologies face challenges in efficiently compressing and transmitting high-dimensional point cloud data, particularly in dynamic scenarios, due to high computational complexity and data volume, which affects real-time applications like autonomous driving and cultural heritage visualization.
Innovation Solution
The method involves reconstructing a geometry-reconstructed point cloud using lossy coding and performing KD-tree searches to determine nearest neighbors for color value averaging, generating an encoded video stream based on these averages, and using a combination of average and median statistics to reduce computational complexity and improve compression efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If lossy coding is used to reconstruct geometry, then data volume is reduced, but color accuracy deteriorates
Solution Approach 1:
The patent introduces an intermediary recoloring process that mediates between the geometry-reconstructed point cloud and the final output. This intermediary step uses KD-tree nearest neighbor searches to find corresponding points in the source point cloud and transfer their color attributes, thereby restoring color accuracy that was lost during lossy geometry compression without increasing the already-reduced data volume
Solution Approach 2:
The patent copies color attributes from source point cloud points to reconstructed point cloud points through the recoloring process. By copying color information from nearest neighbors in the source data, the system restores visual fidelity without copying the full original geometry data, maintaining compression efficiency while improving color accuracy
2Manufacturing precision
If KD-tree searches are performed to improve color accuracy, then color fidelity is improved, but computational complexity increases
Solution Approach 1:
The patent applies partial action by performing KD-tree searches selectively rather than exhaustively for all points. The system determines a sufficient number of nearest neighbors to achieve acceptable color fidelity without searching indefinitely, balancing computational effort with quality improvement. This partial action approach achieves adequate color restoration without the full computational burden of exhaustive nearest neighbor searches
3Manufacturing precision
If more nearest neighbors are considered for color averaging, then color accuracy is improved, but processing time increases
Solution Approach 1:
The patent determines an optimal, limited number of nearest neighbors to consider for color averaging rather than using all available neighbors. This partial action approach achieves sufficient color accuracy by considering only the most relevant nearby points, significantly reducing processing time while maintaining acceptable color fidelity for real-time applications
Data Source
AI summary
Methods and apparatuses of encoding a video stream using video point cloud coding include obtaining a source point cloud; obtaining a geometry-reconstructed point cloud, wherein the geometry-reconstructed point cloud is reconstructed using lossy coding; obtaining a target point included in the geometry-reconstructed point cloud; performing at least one of a forward K-dimensional (KD)-tree search to determine a first plurality of points of the source point cloud which are nearest neighbors to the target point, and a backward KD-tree search to determine a second plurality of points of the source point cloud for which the target point is a nearest neighbor; determining an average color value based on at least one of a first average color value of the first plurality of points and a second average color value of the second plurality of points; selecting a color value for the target point based on the average color value; and generating an encoded video stream based on the selected color value.