Point Cloud Attribute Compression Using Neighbor Prediction
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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 significant storage and network resource requirements.
Innovation Solution
A system that compresses attribute information in point clouds by assigning attribute values to points based on neighboring points, determining predicted attribute values, and encoding attribute correction values, allowing for efficient encoding and decoding of hierarchical levels of detail, using techniques like octree encoding and binary arithmetic encoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If point cloud data is stored and transmitted in original format, then complete attribute information is preserved, but storage space and network resources are excessively consumed
Solution Approach 1:
The patent extracts only the essential attribute information from complete point cloud data. By identifying and removing redundant attributes while retaining key characteristics, the system achieves compression without significant loss of useful information, directly resolving the contradiction between information completeness and storage efficiency
Solution Approach 2:
The patent applies different compression strategies to different regions of the point cloud based on local characteristics. Important regions with high detail requirements maintain higher attribute quality, while less critical regions use more aggressive compression, optimizing the balance between information preservation and storage reduction
2Loss of information
If point cloud data is stored and transmitted in original format, then data quality is maintained, but transmission time and processing cost increase
Solution Approach 1:
The patent extracts only the most critical attribute information needed for maintaining data quality perception. By removing redundant attributes that contribute minimally to perceived quality, the system reduces transmission data volume and time while preserving essential quality characteristics
Solution Approach 2:
The patent transmits a partial set of attribute information that is sufficient for maintaining acceptable data quality rather than transmitting complete original data. This partial action approach achieves the necessary quality level with reduced transmission time and resources
3Productivity
If compression is applied to reduce storage and transmission requirements, then resource efficiency improves, but attribute information accuracy may be lost
Solution Approach 1:
The patent applies selective compression where attribute precision is maintained in critical regions and relaxed in less important regions. This local differentiation allows higher overall compression efficiency while preserving accuracy where it matters most, resolving the contradiction between compression efficiency and precision
Solution Approach 2:
The patent changes the precision parameters of attribute information based on importance and context. By adjusting attribute value precision dynamically rather than uniformly, the system achieves better compression efficiency while maintaining necessary accuracy levels for different types of attribute data
Data Source
AI summary
A system comprises an encoder configured to compress attribute information for a point cloud and/or a decoder configured to decompress compressed attribute information for the point cloud. Attribute values for at least one starting point are included in a compressed attribute information file and attribute correction values used to correct predicted attribute values are included in the compressed attribute information file. Attribute values are predicted based, at least in part, on attribute values of neighboring points and distances between a particular point for whom an attribute value is being predicted and the neighboring points. The predicted attribute values are compared to attribute values of a point cloud prior to compression to determine attribute correction values. A decoder follows a similar prediction process as an encoder and corrects predicted values using attribute correction values included in a compressed attribute information file.