Adaptive Point Cloud Compression Using Patch-Based Image Filtering
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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 data volume and storage requirements.
Innovation Solution
A system that compresses point cloud data by projecting points onto patch planes, generating patch images with spatial and depth information, and encoding these images using video encoding techniques, allowing for efficient storage and transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If point cloud data is stored and transmitted in its original form, then data quality and completeness are preserved, 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 local region of the point cloud. This segmentation allows for selective processing and compression of different regions, enabling efficient storage and transmission while preserving important geometric features in each patch
Solution Approach 2:
The patent transforms 3D point cloud data into 2D patch representations by projecting points onto patch planes. This dimensionality reduction compresses the data structure while maintaining essential spatial relationships, thereby reducing storage requirements and transmission time without significant loss of quality
2Productivity
If point cloud data is compressed to reduce storage needs, then transmission efficiency improves, but data quality may deteriorate
Solution Approach 1:
Different patches are processed with different compression strategies based on their local characteristics. Important regions with high geometric complexity or semantic significance receive higher preservation priority, while less critical regions undergo more aggressive compression, achieving overall efficiency without sacrificing essential quality
Solution Approach 2:
The patent employs iterative optimization where compression parameters are adjusted based on feedback from quality assessment metrics. The system evaluates the impact of compression on point cloud quality and adapts the compression strength accordingly, ensuring optimal balance between transmission efficiency and data quality
3Reliability
If complex compression algorithms are used to minimize data loss, then data quality is preserved, but processing complexity and computational cost increase
Solution Approach 1:
By dividing the point cloud into patches, the patent reduces the computational complexity of processing the entire dataset at once. Each patch can be independently compressed using simpler algorithms, and the parallel processing of multiple patches reduces overall computational burden while maintaining quality
Solution Approach 2:
The transformation to 2D patch representations simplifies the data structure, enabling the use of efficient 2D image compression techniques rather than complex 3D point cloud algorithms. This dimensionality change reduces processing complexity while preserving essential geometric information
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. A processing/filtering element utilizes occupancy map information and/or auxiliary patch information to determine relationships between patches in image frames and adjusts encoding/decoding and/or filtering or pre/post-processing parameters based on the determined relationships.