Point Cloud Compression Using Weighted Color Transfer
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Solution Overview
Problem
Current technologies face challenges in efficiently compressing and decompressing point clouds, which are large datasets used for 3D representations, particularly in real-time communications and applications like virtual reality, due to high data volume and complexity.
Innovation Solution
The implementation of point cloud compression/decompression methods using processing circuitry that determines weighted color averages based on location and color differences between candidate source points and a target point, enabling efficient encoding and decoding of point cloud data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If point cloud data is compressed using traditional methods, then data volume is reduced, but texture quality and color accuracy deteriorate
Solution Approach 1:
The patent introduces an intermediary color transfer process between geometry reconstruction and final texture encoding. Instead of directly encoding compressed texture data, the system first reconstructs geometry, then transfers colors from source point cloud to reconstructed points using weighted averaging of candidate source points. This intermediary step preserves texture quality while enabling compression of the original point cloud data.
Solution Approach 2:
The patent creates a copy of the source point cloud's color information and transfers it to the reconstructed geometry. Rather than directly compressing and transmitting all original data, the system copies essential color attributes from source points to reconstructed points using a weighted color transfer algorithm, maintaining visual fidelity while reducing data transmission requirements.
2Speed
If point cloud data is transmitted in real-time, then application responsiveness is improved, but data transmission complexity and bandwidth requirements increase
Solution Approach 1:
The patent segments the point cloud data processing into distinct independent stages: geometry compression and transmission, geometry reconstruction at the decoder, and color transfer from source to reconstructed points. This segmentation allows parallel processing and optimization of each stage, reducing overall complexity while enabling real-time transmission of compressed geometry data with subsequent color restoration.
Solution Approach 2:
The patent performs preliminary compression of geometry data before transmission, while the computationally intensive color transfer operation is performed as a preliminary action at the decoder side before final rendering. This preliminary action approach shifts complexity to the decoding end, enabling faster real-time transmission of compressed data while maintaining quality through pre-computed color transfers.
Data Source
AI summary
Aspects of the disclosure provide methods and apparatuses for point cloud compression and decompression. In some examples, an apparatus for point cloud compression/decompression includes processing circuitry. For example, the processing circuitry determines, from a point cloud, at least a first set of candidate source points that are associated with a target point in a geometry image reconstructed from a compressed geometry image for the point cloud. Then, the processing circuitry weights a color of each candidate source point in the first set based on a combination of a location difference between the candidate source point and the target point, and a color difference between the color of the candidate source point and an aggregate color of the first set of candidate source points. Further, the processing circuitry determines a color of the target point based on a first weighted color average of the first set of candidate source points.