Patch Image Compression of Point Clouds for Real-Time 3D Transmission
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Solution Overview
Problem
Point clouds generated by sensors like LIDAR systems are large and costly to store and transmit, limiting their use in real-time applications due to high storage and network resource requirements.
Innovation Solution
A system that compresses point cloud data by projecting points onto patch planes, generating patch images for spatial and attribute information, and encoding these images using video encoding standards like HEVC, allowing for efficient storage and transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If point cloud data is stored and transmitted in original format, then data quality and completeness are maintained, but storage costs and network resource requirements increase significantly
Solution Approach 1:
The point cloud data is divided into multiple patches, where each patch represents a local region of the point cloud. This segmentation allows for independent processing and compression of different regions, enabling efficient storage and transmission while maintaining the essential geometric and attribute information of each patch
Solution Approach 2:
The patent transforms 3D point cloud data into 2D patch images through projection operations. By converting spatial point coordinates into 2D image representations with associated depth and attribute information, the data structure is optimized for compression while preserving the essential visual and geometric characteristics of the original point cloud
2Productivity
If point cloud data is compressed using projection methods, then storage and transmission efficiency improve, but data processing complexity increases
Solution Approach 1:
The patent performs preliminary organization of point cloud data into patches and generates corresponding patch images, depth maps, and attribute maps before compression. This pre-processing step structures the data in a way that facilitates efficient compression and enables standard video coding techniques to be applied directly, reducing the overall processing complexity during transmission and decoding
Solution Approach 2:
The patent introduces patch images as an intermediary representation between the original 3D point cloud and the compressed data format. These 2D images serve as a bridge that allows standard video compression algorithms to process point cloud data effectively, simplifying the compression process while maintaining data quality
3Quantity of substance
If real-time communication of point cloud data is enabled through compression, then network resource requirements decrease, but decompression and reconstruction time increase
Solution Approach 1:
By dividing the point cloud into multiple independent patches that can be compressed and transmitted separately, the system enables parallel decompression processing. This segmentation reduces the overall decompression time as multiple patches can be reconstructed simultaneously rather than sequentially, making real-time communication feasible
Data Source
AI summary
A system comprises an encoder configured to compress attribute information and/or spatial for a point cloud and/or a decoder configured to decompress compressed attribute and/or spatial information for the point cloud. To compress the attribute and/or spatial information, the encoder is configured to convert a point cloud into an image based representation. Also, the decoder is configured to generate a decompressed point cloud based on an image based representation of a point cloud.