Hierarchical Point Cloud Compression via Space-Filling Curve
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Solution Overview
Problem
Point cloud data, captured by sensors like LIDAR systems, is voluminous and costly to store and transmit, limiting its 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 its original format, then data quality and completeness are maintained, but storage cost and network resource consumption increase significantly
Solution Approach 1:
The patent extracts only the essential attribute information from each point cloud point while omitting redundant spatial coordinates. By taking out only the necessary attribute data (color, intensity, reflectivity, motion attributes) and discarding the full spatial information, the system achieves significant compression of data volume while maintaining the essential characteristics needed for applications like augmented reality and virtual reality.
Solution Approach 2:
The patent creates a compressed representation or copy of the point cloud data that retains the essential attribute information without the full spatial detail. This compressed copy is sufficient for many applications, allowing the system to work with a reduced data set that maintains functional equivalence for the intended use cases.
2Productivity
If point cloud data is processed in real-time, then application responsiveness is improved, but computational resource requirements and data processing complexity increase
Solution Approach 1:
By extracting only the essential attribute information from each point cloud point, the patent reduces the amount of data that needs to be processed in real-time. This extraction of critical attributes (color, intensity, reflectivity, motion) while omitting redundant spatial coordinates significantly decreases computational complexity while maintaining real-time processing capability for applications like augmented reality and virtual reality.
Solution Approach 2:
The patent segments the point cloud data into individual points with their associated attributes, allowing for parallel processing and efficient real-time computation. This segmentation enables the system to process each point independently using simple attribute-based operations, significantly reducing overall processing complexity while maintaining real-time performance.
3Quantity of substance
If all point cloud attributes are transmitted, then data completeness is maintained, but network bandwidth requirements and transmission cost increase
Solution Approach 1:
The patent extracts only the essential attribute information from each point cloud point while omitting redundant spatial coordinates. By taking out only the necessary attribute data (color, intensity, reflectivity, motion attributes) and discarding the full spatial information, the system achieves significant compression of data volume while maintaining the essential characteristics needed for applications like augmented reality and virtual reality.
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. To compress the attribute information, multiple levels of detail are generated based on an ordering of the points according to a space filling curve and attribute values are predicted. The attribute values may be predicted simultaneously while points are being assigned to different levels of detail. A decoder follows a similar prediction process based on level of details. Also, attribute correction values may be determined to correct predicted attribute values and may be used by a decoder to decompress a point cloud compressed using level of detail attribute compression. In some embodiments, attribute correction values may take into account an influence factor of respective points in a given level of detail on attributes in other levels of detail.