Point Cloud Patch Boundary Smoothing for V-PCC Compression
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Solution Overview
Problem
Current technologies face challenges in efficiently compressing point clouds for real-time applications in virtual reality and dynamic mapping for autonomous driving, while also requiring lossless compression for cultural heritage applications.
Innovation Solution
The proposed solution leverages existing video codecs to compress the geometry, occupancy, and texture of dynamic point clouds as separate video sequences, with additional metadata compression to efficiently handle the compression of point clouds using video-based point cloud compression (V-PCC) techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If point clouds are compressed using traditional methods, then data reduction is achieved, but reconstruction quality and visual fidelity deteriorate at patch boundaries
Solution Approach 1:
The patent divides the point cloud into multiple patches and identifies boundary points between patches. By segmenting the processing into intra-patch points and boundary points, the method applies different smoothing operations to different regions, thereby maintaining high reconstruction quality at boundaries while achieving overall data compression.
Solution Approach 2:
The patent applies different smoothing strategies to different regions: intra-patch points use one smoothing approach while boundary points use a different approach that considers neighboring patches. This local differentiation ensures that reconstruction quality is maintained where it matters most (at boundaries) while still achieving compression overall.
2Speed
If real-time compression is implemented for virtual reality applications, then processing speed is improved, but compression efficiency and lossless reconstruction are compromised
Solution Approach 1:
The patent performs boundary point identification and smoothing parameter determination as preliminary steps before the actual compression and reconstruction processes. By preparing these critical parameters in advance, the method enables faster real-time processing while maintaining high compression efficiency and minimizing information loss.
Solution Approach 2:
The patent uses reconstructed point clouds as feedback to determine smoothing parameters for boundary points. This feedback mechanism allows the system to adaptively optimize compression efficiency while maintaining reconstruction quality, achieving both real-time performance and high fidelity.
3Quantity of substance
If aggressive compression is applied to reduce data size, then storage and transmission requirements are reduced, but visual quality and color fidelity at patch boundaries deteriorate
Solution Approach 1:
The patent applies targeted smoothing operations specifically at patch boundaries where color discontinuities occur, while using less intensive methods for intra-patch points. This local quality approach maintains color fidelity at critical boundary regions while achieving overall data size reduction through selective processing.
Solution Approach 2:
The patent dynamically adjusts smoothing parameters based on the specific characteristics of boundary points and their neighboring patches. By changing parameters adaptively rather than applying fixed compression, the method maintains color fidelity at boundaries while achieving efficient data size reduction.
Data Source
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AI summary
A method and apparatus for decoding a video stream encoded using video point cloud coding, the decoding including obtaining a geometry-reconstructed point cloud based on one or more patches; identifying a first boundary of a patch including a plurality of first boundary points; identifying a second boundary including a plurality of second boundary points inside the first boundary; performing smoothing on the first boundary points and the second boundary points; obtaining a smoothed geometry-reconstructed point cloud based on the smoothed first boundary points and the smoothed second boundary points; and reconstructing a dynamic point cloud using the smoothed geometry-reconstructed point cloud.