Point Cloud Compression Using Fixed-Point Arithmetic
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Solution Overview
Problem
Point cloud files, 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 attribute information of point clouds using a space-filling curve ordering, predicting attribute values based on neighboring points, and applying attribute correction values, allowing for efficient storage and transmission of point cloud data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If point cloud data is stored and transmitted in original format, then data quality and completeness are preserved, but storage cost and transmission time increase significantly
Solution Approach 1:
The patent segments point cloud data into multiple levels of detail (LODs) based on spatial position and attribute characteristics. Points are organized into coarse and fine levels, allowing selective transmission of essential information while reducing overall data volume. This segmentation enables quality-preserving compression by maintaining critical geometric and attribute data while discarding redundant information.
Solution Approach 2:
The patent applies different compression strategies to different regions of the point cloud based on local attribute variations. Areas with high attribute diversity require higher precision, while uniform regions can be compressed more aggressively. This local quality approach maintains data quality where needed while significantly reducing storage requirements overall.
2Productivity
If attribute information is compressed using prediction and correction methods, then storage and transmission efficiency improve, but processing complexity increases
Solution Approach 1:
The patent performs preliminary organization of points into space-filling curve order and pre-computation of neighboring point relationships before compression. This preliminary action simplifies the subsequent prediction process by establishing a predetermined processing sequence and neighborhood structure, reducing the computational complexity during actual compression operations.
Solution Approach 2:
The compression system uses the point cloud's own spatial and attribute characteristics to generate prediction values. Neighboring points in the space-filling curve order provide self-generated predictions that leverage the data's inherent structure, eliminating the need for external complex prediction models and reducing processing complexity.
3Measurement precision
If points are organized according to space filling curve, then attribute prediction accuracy improves, but ordering computation time increases
Solution Approach 1:
The patent uses a predetermined space-filling curve pattern (such as Morton ordering) that can be replicated without recomputation. Instead of calculating the space-filling curve order dynamically for each point cloud, the system applies a standard ordering formula that generates the same sequence for points with comparable spatial characteristics, significantly reducing computation time while maintaining prediction accuracy.
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. Attribute values for at least one starting point are included in a compressed attribute information file and attribute correction values are included in the compressed attribute information file. Attribute values are predicted based, at least in part, on attribute values of neighboring points. The predicted attribute values are compared to attribute values of a point cloud prior to compression to determine attribute correction values. In order to improve computing efficiency and/or repeatability, fixed-point number representations are used when determining predicted attribute values and 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 using fixed-point number representations.