Geometry Smoothing by Local Projection for Point Cloud Compression
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Solution Overview
Problem
Current technologies face challenges in efficiently compressing point clouds for real-time communications in virtual reality and dynamic mapping applications, requiring effective lossy and lossless compression methods to reduce data volume while maintaining quality.
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 optimize data transmission and decoding processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If point cloud data is compressed for real-time communication, then data transmission efficiency is improved, but reconstruction quality deteriorates
Solution Approach 1:
The point cloud is divided into multiple patches, and each patch is processed independently through projection onto 2D images. This segmentation allows efficient compression of each patch while maintaining overall reconstruction quality through localized processing.
Solution Approach 2:
The patent transforms 3D point cloud data into 2D image projections for compression, then reconstructs the 3D structure from these 2D representations. This dimensionality change enables leveraging efficient 2D image compression algorithms while preserving 3D geometric information.
2Quantity of substance
If aggressive compression is applied to reduce data volume, then bandwidth requirements are reduced, but geometric accuracy deteriorates
Solution Approach 1:
The patent creates 2D image copies/projections of the 3D point cloud patches. These 2D representations serve as compressed intermediaries that capture essential geometric information with reduced data volume, which can then be used to reconstruct the original 3D structure with maintained accuracy.
3Loss of time
If point cloud data is processed in real-time, then communication latency is reduced, but processing complexity increases
Solution Approach 1:
The patent replaces complex 3D point cloud processing operations with 2D image processing operations. By substituting 3D geometric computations with 2D image compression and transformation algorithms, the system achieves real-time processing efficiency while maintaining geometric fidelity.
Data Source
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AI summary
Methods and apparatuses of decoding a video stream encoded using video point cloud coding include obtaining a geometry-reconstructed point cloud; dividing the geometry-reconstructed point cloud into a plurality of cells, wherein a cell of the plurality of cells includes a first plurality of points; determining a plane representing the first plurality of points; generating a second plurality of points by projecting the first plurality of points onto the plane; obtaining a smoothed geometry-reconstructed point cloud based on the second plurality of points; and reconstructing a dynamic point cloud using the smoothed geometry-reconstructed point cloud.