Point Cloud Patch Image Compression for Real-Time 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 uncompressed form, then data quality and completeness are maintained, but storage space and network bandwidth 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 independent processing and compression of different regions, enabling efficient storage and transmission while maintaining data quality through localized optimization strategies
Solution Approach 2:
The patent transforms 3D point cloud data into 2D patch images through projection operations. This dimensionality reduction converts spatial coordinates (x, y, z) into 2D image coordinates with depth information, enabling the use of efficient 2D image compression algorithms while preserving the essential geometric and attribute information of the original point cloud
2Quantity of substance
If point cloud data is compressed using traditional methods, then storage requirements are reduced, but compression efficiency is insufficient for large-scale point clouds
Solution Approach 1:
By dividing the point cloud into patches, the system can apply compression algorithms independently to each patch, improving parallel processing efficiency and reducing the computational burden on single large datasets. This segmentation enables more effective memory management and faster compression speeds
Solution Approach 2:
The patent replaces traditional point cloud compression algorithms with video encoding standards (H.264/AVC, H.265/HEVC). This substitution leverages highly optimized video compression technology that has been extensively developed for decades, achieving superior compression efficiency and faster processing speeds compared to conventional point cloud methods
3Productivity
If video encoding standards are used to compress point cloud patch images, then compression efficiency and speed improve, but encoding complexity increases
Solution Approach 1:
The patent leverages the universality of video encoding standards, which are already widely implemented in various devices and software. By reusing existing H.264/HEVC encoders, the system gains access to highly optimized, battle-tested compression technology without developing complex custom encoding algorithms from scratch
Solution Approach 2:
The approach copies the successful methodology of video compression and adapts it for point cloud data. By treating point cloud patches as video frames and applying established video encoding standards, the system benefits from decades of optimization in codec development, hardware acceleration, and software implementation
4Speed
If point clouds are transmitted in real-time, then application responsiveness improves, but network bandwidth consumption increases
Solution Approach 1:
The patent transforms the data representation parameters from 3D point coordinates to 2D image formats with depth maps. This parameter change enables the application of efficient video compression techniques that achieve high compression ratios, reducing the amount of data that needs to be transmitted over the network while maintaining real-time transmission capabilities
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.