Point Cloud Projection Into Patch Images for Real-Time Compression
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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 patches, generating patch images with spatial and depth information, and encoding these images using video encoding standards like HEVC, allowing for efficient storage and transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If point cloud data is stored and transmitted in original format, then data quality is preserved, but storage space and network bandwidth requirements increase significantly
Solution Approach 1:
The point cloud data is divided into multiple patches or regions, each processed independently through projection and encoding. This segmentation allows for localized optimization of compression parameters while maintaining overall data quality, reducing the total data size without significant loss of information.
Solution Approach 2:
The patent creates projected representations (2D images) as copies of the original 3D point cloud data. These projected copies contain essential spatial and attribute information in a compressed format, allowing the original high-precision data to be replaced with smaller projected data for storage and transmission purposes.
2Quantity of substance
If point cloud data is compressed using projection methods, then data size is reduced, but processing complexity increases
Solution Approach 1:
The patent transforms 3D point cloud data into 2D projected images, changing the dimensional representation. This dimensionality reduction simplifies the data structure and enables the use of efficient 2D image compression algorithms, reducing processing complexity compared to direct 3D compression methods.
Solution Approach 2:
The patent replaces complex 3D geometric processing with 2D image processing operations. By substituting mechanical 3D coordinate transformations with optical-like projection and standard image encoding techniques, the processing complexity is significantly reduced while maintaining compression effectiveness.
3Quantity of substance
If video encoding standards are used for point cloud compression, then compression efficiency is improved, but encoding time increases
Solution Approach 1:
The patent applies universal video encoding standards (designed for 2D images) to 3D point cloud data through projection. This multi-functionality approach allows the reuse of highly optimized video coding algorithms for a different data type, achieving high compression ratios without developing specialized 3D encoders from scratch.
Solution Approach 2:
The patent performs preliminary projection of 3D point cloud data into 2D images before encoding. This preliminary transformation prepares the data in a format suitable for fast video encoding, separating the geometric transformation step from the compression step and enabling the use of rapid standardized encoders.
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.