Point Cloud Compression via Patch-Based Encoding and Non-Jumpy Scanning
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Solution Overview
Problem
Current point cloud compression technologies face challenges in efficiently reducing data volume for real-time applications like virtual reality and autonomous driving, requiring effective lossy and lossless compression methods to manage large datasets.
Innovation Solution
The proposed solution involves processing circuitry that determines missed points in a point cloud, orders them in a non-jumpy sequence, and encodes them into a bitstream, using existing video codecs for compression and decompression, leveraging techniques like patch-based encoding and non-jumpy scanning to improve compression efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If point cloud data is compressed using traditional methods, then data volume is reduced, but compression efficiency and quality are insufficient for real-time applications
Solution Approach 1:
The point cloud is divided into multiple patches, and each patch is processed independently through encoding into image format. This segmentation allows for more efficient compression while maintaining local geometric details, resolving the contradiction between compression efficiency and quality preservation.
Solution Approach 2:
The patent transforms 3D point cloud data into 2D image format for compression, then reconstructs the 3D structure. This dimensional transformation enables the use of efficient 2D image compression algorithms while preserving 3D geometric information, improving compression efficiency without significant quality loss.
2Speed
If point cloud data is compressed for real-time applications, then processing speed is improved, but data accuracy may be compromised
Solution Approach 1:
The patent performs preliminary organization of missed points using non-jumpy scan ordering before compression. This preprocessing step ensures that spatially correlated points are grouped together, enabling more efficient compression and faster processing while maintaining point cloud accuracy through structured data organization.
3Ease of manufacture
If traditional compression algorithms are used, then implementation is simple, but compression ratio is insufficient
Solution Approach 1:
The patent leverages existing video codec infrastructure to perform point cloud compression, making the system universally applicable and easier to implement. By reusing established video compression technologies, the patent achieves high compression ratios without requiring entirely new algorithms, balancing implementation simplicity with effective data volume reduction.
Data Source
AI summary
According to some aspects of the disclosure, processing circuitry for point cloud decompression can decode prediction information of an image from a coded bitstream corresponding to a point cloud. The prediction information indicates that the image includes a first portion corresponding to patches in the point cloud, and a second portion corresponding to missed points by the patches from the point cloud. The processing circuitry can scan the second portion of the image according to a non-jumpy scan to obtain the missed points in a sequence, and reconstruct the point cloud based on reconstructed patches corresponding to the patches and reconstructed missed points based on the sequence of the missed points.