Point Cloud Compression via Patch Image Projection
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Solution Overview
Problem
Point clouds generated by sensors like LIDAR systems and 3D cameras contain large amounts of spatial and attribute information, making them costly and time-consuming to store and transmit, which limits their use in real-time applications due to significant storage and network resource requirements.
Innovation Solution
A system that compresses point cloud data by dividing it into patches, projecting points onto patch planes, generating patch images with depth information, and encoding these images using video compression standards like HEVC, while accounting for distortion through a closed-loop color conversion module to minimize data size and maintain quality.
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 requirements 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 efficient processing and compression while maintaining the overall data quality. The encoder processes each patch independently, creating patch images that can be compressed using standard image coding techniques.
Solution Approach 2:
The patent creates patch images as 2D representations of 3D point cloud patches. These patch images serve as compressed copies of the original point cloud data, maintaining the essential geometric and attribute information in a more compact format that can be efficiently stored and transmitted.
2Quantity of substance
If point cloud data is compressed to reduce storage needs, then storage efficiency improves, but data quality and geometric accuracy may deteriorate
Solution Approach 1:
The patent employs a closed-loop color conversion module that provides feedback to minimize distortion during compression. The module monitors the compression process and adjusts parameters to maintain geometric accuracy and attribute fidelity, ensuring that the compressed patch images accurately represent the original point cloud data.
Solution Approach 2:
The encoder transforms point cloud data into different parameter representations through patch image generation. By changing the representation from 3D point coordinates to 2D patch images with associated depth and attribute maps, the data becomes more amenable to compression while preserving essential geometric and visual information.
3Productivity
If point cloud data is transmitted in real-time, then application responsiveness improves, but network bandwidth requirements increase
Solution Approach 1:
By segmenting the point cloud into patches and representing each as a compact patch image, the total data volume is significantly reduced. This segmentation enables real-time transmission over limited network bandwidth while maintaining the ability to process and reconstruct the point cloud data on the receiving end.
4Quantity of substance
If patch images are encoded using video compression standards, then compression efficiency improves, but computational complexity increases
Solution Approach 1:
The patent leverages existing video compression standards (such as H.264/AVC or H.265/HEVC) to encode patch images. By reusing established video coding tools and algorithms, the system achieves high compression ratios without developing proprietary compression methods, balancing computational complexity with encoding efficiency.
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 block/sub-block organization scheme is used to encode blocks and sub-blocks of an occupancy map used in compressing the point cloud. Binary values are assigned to blocks/sub-blocks based on whether they contain patches projected on the point cloud. A traversal path is chosen that takes advantage of run-length encoding strategies to reduce a size of an encoded occupancy map. Also, auxiliary information is used to further improve occupancy map compression.