Point Cloud Compression via Video Coding and Missed Point Reconstruction
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Solution Overview
Problem
Current technologies face challenges in efficiently compressing point clouds, particularly for real-time communications in virtual reality and dynamic mapping applications, due to the large data volumes and complexity of representing 3D scenes with high geometric and color accuracy.
Innovation Solution
The proposed solution leverages video-coding techniques to compress the geometry, occupancy, and texture of point clouds as separate video sequences, using existing video codecs to reduce data requirements, and incorporates metadata compression to enhance efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If point cloud data is represented with high geometric and color accuracy, then visual quality is improved, but data volume increases making storage and transmission costly and time-consuming
Solution Approach 1:
The patent segments the point cloud data into multiple patches, where each patch is processed and compressed independently. This segmentation allows for efficient encoding of geometric and color attributes separately, reducing overall data volume while maintaining high accuracy through localized processing of each patch region
Solution Approach 2:
The patent transforms 3D point cloud data into 2D image representations by projecting points onto image planes. This dimensionality reduction from 3D to 2D space enables the use of efficient 2D image compression algorithms while preserving geometric and color information, significantly reducing data volume for storage and transmission
2Ease of operation
If conventional scanning methods are used for point cloud compression, then encoding simplicity is maintained, but missed points cannot be accurately reconstructed leading to quality loss
Solution Approach 1:
The patent performs preliminary organization of missed points into image structures before compression. By pre-arranging missed points in 2D image formats with proper spatial relationships, the system enables accurate reconstruction during decoding while maintaining encoding simplicity through standardized image processing operations
Solution Approach 2:
The patent introduces 2D image representations as an intermediary between the original 3D point cloud and the compressed data. This intermediary format preserves spatial relationships of missed points while enabling efficient compression, allowing accurate reconstruction without complex encoding procedures
3Quantity of substance
If video-coding techniques are applied to compress point cloud geometry and texture, then data volume is reduced, but processing complexity increases
Solution Approach 1:
The patent applies universal video-coding techniques to point cloud data by treating geometry and texture as video sequences. This multi-functional approach uses existing standardized video compression algorithms to handle point cloud data, reducing processing complexity while achieving significant data volume reduction through proven compression methods
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 apparatus is for point cloud decompression. The processing circuitry decodes prediction information of an image from a coded bitstream corresponding to a point cloud. The prediction information indicates that the image includes a plurality of missed points from at least a patch for the point cloud, and the plurality of missed points are arranged in the image according to a non-jumpy scan. Then, the processing circuitry reconstructs the plurality of missed points from the image according to the non-jumpy scan.