Adaptive Patch Filtering in Point Cloud Image 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 data volume and storage 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 using video encoding techniques to pack and decode these images efficiently, allowing for real-time transmission and processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If point cloud data is stored and transmitted in its original format, then data完整性 (data completeness) is maintained, but storage cost 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 transmission of only relevant patches, reducing overall transmission time while maintaining data completeness when all patches are reconstructed at the decoder.
Solution Approach 2:
The patent transforms 3D point cloud data into 2D patch images by projecting points onto a 2D plane. This dimensionality reduction compresses the data structure, enabling more efficient storage and transmission while preserving the essential spatial and attribute information needed for reconstruction.
2Loss of information
If all point cloud attributes are transmitted, then information completeness is maintained, but data volume increases
Solution Approach 1:
The patent extracts only the essential spatial coordinates and key attribute information from the full point cloud data, packaging them into patch images. Non-essential or redundant attributes are omitted or compressed, reducing data volume while maintaining the information needed for accurate reconstruction of the point cloud.
Solution Approach 2:
The patent transforms point cloud attributes into image-compatible parameter formats (e.g., converting 3D coordinates to 2D pixel positions, encoding color attributes as image pixel values). This parameter transformation enables the use of efficient image compression algorithms while preserving the semantic meaning of the original attributes.
3Measurement precision
If high-resolution point cloud data is processed, then spatial precision is improved, but processing time increases
Solution Approach 1:
High-resolution point cloud data is divided into multiple patches, allowing parallel processing of each patch independently. This segmentation enables the system to maintain high spatial precision within each patch while significantly reducing overall processing time through parallel computation and selective processing of only relevant patches.
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.