Point Cloud Compression via Patch Image Projection
Find Innovative SolutionsGenerate Solutions
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 techniques 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 completeness and quality are maintained, but storage costs and transmission time increase significantly
Solution Approach 1:
The point cloud data is divided into multiple patches, where each patch represents a localized region of the point cloud. This segmentation allows selective encoding and transmission of only relevant patches, reducing overall transmission time while maintaining data completeness for visible regions.
Solution Approach 2:
The patent extracts and transmits only the necessary point cloud data corresponding to visible surfaces and regions of interest, rather than transmitting the entire point cloud. This extraction approach maintains data completeness for visible areas while significantly reducing transmission time and bandwidth requirements.
2Productivity
If point cloud data is compressed using traditional methods, then transmission efficiency improves, but data quality and reconstruction accuracy deteriorate
Solution Approach 1:
The patent projects 3D point cloud data onto 2D image planes, transforming the data representation from three-dimensional to two-dimensional space. This dimensionality change enables the use of efficient 2D image compression algorithms while preserving sufficient geometric information for accurate reconstruction, thus improving transmission efficiency without significantly compromising reconstruction accuracy.
Solution Approach 2:
The patent utilizes depth information as an additional parameter to enhance the compression process. By incorporating depth values along with color information in the projected image planes, the method achieves better compression ratios while maintaining reconstruction accuracy, as the depth parameter provides explicit geometric information that aids in accurate 3D reconstruction.
3Reliability
If all point cloud attributes are encoded, then data fidelity is maintained, but encoding complexity and processing time increase
Solution Approach 1:
The patent extracts and encodes only the most essential attributes (color and depth) from the full set of point cloud attributes. By selecting and encoding only these critical attributes in the projected image planes, the method maintains data fidelity for the most important visual properties while significantly reducing encoding complexity and processing requirements.
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.