Point Cloud Compression via Video Coding and Local Color Interpolation
Find Innovative SolutionsGenerate Solutions
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 proposed solution utilizes video-coding techniques to compress the geometry, occupancy, and texture of point clouds as separate video sequences, leveraging existing video codecs and metadata compression to reduce data volume while maintaining quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If point cloud data is stored and transmitted with high quality representation, then the accuracy and detail of 3D reconstruction is improved, but the data volume and transmission cost increase significantly
Solution Approach 1:
The point cloud data is segmented into multiple patches, where each patch represents a local region of the 3D space. This segmentation allows independent processing and compression of different regions, enabling efficient representation of large point clouds while maintaining local geometric accuracy and color information.
Solution Approach 2:
The patent transforms 3D point cloud data into 2D geometry images and texture images through projection. This dimensionality reduction from 3D to 2D space enables efficient compression and transmission while preserving the essential geometric and visual information needed for accurate 3D reconstruction.
2Measurement precision
If more candidate source points are used for color determination, then the color accuracy of reconstructed points is improved, but the computational complexity increases
Solution Approach 1:
The patent applies different processing strategies to different regions by identifying candidate source points locally around each target point. The color transfer process considers spatial locality by selecting candidate points from neighboring regions, ensuring that color information is accurately transferred while maintaining computational efficiency through localized processing.
Solution Approach 2:
The patent uses distance-weighted averaging to determine colors, where the weight parameter is inversely proportional to the distance between candidate source points and target points. This parameter-based weighting scheme automatically prioritizes closer points while still considering multiple candidates, achieving color accuracy without requiring exhaustive search of all possible source points.
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 of the apparatus for point cloud encoding determines, from a point cloud, more than two candidate source points that are associated with a target point in a reconstructed geometry reconstructed from a compressed geometry image for the point cloud. Then the processing circuitry determines a color for the target point based on colors of the more than two candidate source points, and encodes texture of the point cloud with the target point having the determined color.