Point Cloud Compression via 3D to 2D Patch Image Conversion
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 dividing it into patches, generating patch images with spatial and depth information, and encoding these images using video compression 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 costs and transmission time increase significantly
Solution Approach 1:
The point cloud data is divided into multiple patches, where each patch contains points with surface normal vectors that deviate from one another less than a threshold amount. This segmentation allows for localized processing and compression while maintaining overall data quality.
Solution Approach 2:
The patent creates patch images that are 2D representations of 3D point cloud patches. These patch images serve as compressed copies that can be stored and transmitted efficiently while preserving the essential spatial and attribute information of the original point cloud data.
2Quantity of substance
If point cloud data is compressed to reduce storage costs, then storage and transmission efficiency improve, but data processing complexity increases
Solution Approach 1:
The patent transforms 3D point cloud data into 2D patch images by changing the dimensional parameters. This transformation reduces data volume while the structured format of patch images (with defined coordinate systems and projection methods) simplifies subsequent processing compared to raw 3D point cloud manipulation.
Solution Approach 2:
The patent replaces complex 3D spatial data structures with 2D image representations. This substitution leverages成熟 image processing algorithms and data structures, reducing processing complexity while maintaining the essential geometric and attribute information.
3Quantity of substance
If point cloud data is compressed using traditional methods, then data volume is reduced, but real-time processing capability is lost
Solution Approach 1:
By dividing the point cloud into patches and representing each as a separate image, the data structure enables parallel processing. Multiple patches can be processed simultaneously, maintaining real-time processing capability while achieving compression.
Solution Approach 2:
The patch image format serves multiple functions: it compresses data, maintains spatial relationships, preserves attribute information, and enables efficient processing. This multi-functional approach supports real-time applications by eliminating the need for separate processing pipelines.
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. In some embodiments, an encoder may be configured to further compress points omitted from the image based representation. Also, in some embodiments, a decoder may be configured to decode points compressed outside of an image based representation or in a separate image based representation.