Serial Point Cloud Compression Through Change-Based Versioning
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Solution Overview
Problem
Existing techniques for storing and managing 3D coordinate point clouds are inefficient, requiring significant storage and bandwidth resources due to the need to store and transmit redundant data when comparing multiple point clouds captured at different times and locations, without effectively identifying and storing only the changes between them.
Innovation Solution
A method and system for point cloud versioning that aligns and compares two point clouds to identify changes, generating a new point cloud that includes only the differences, thereby reducing redundant data storage and transmission by storing and transmitting only the identified changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If all point cloud data is stored and transmitted, then complete data availability is achieved, but storage and bandwidth resources are significantly consumed
Solution Approach 1:
The patent extracts only the changed portions between point clouds rather than storing complete data. By identifying and isolating differences between sequential point clouds, the system stores only the essential information needed to represent changes, eliminating redundant unchanged data while maintaining data completeness for change detection purposes.
Solution Approach 2:
The patent discards redundant unchanged data between point clouds and recovers the complete information when needed by combining the stored differences with the base point cloud. This approach allows efficient storage of only necessary change information while enabling full reconstruction of updated point clouds when required.
2Reliability
If redundant data is stored for comparing multiple point clouds, then change detection capability is maintained, but storage efficiency deteriorates
Solution Approach 1:
The patent segments the point cloud data into unchanged portions and changed portions. By dividing the data representation into these distinct segments, the system stores only the changed segments while maintaining the ability to detect changes accurately by comparing against the base point cloud or previously stored versions.
3Loss of information
If complete point clouds are transmitted, then data integrity is ensured, but bandwidth consumption increases
Solution Approach 1:
The patent extracts and transmits only the changed portions between point clouds rather than complete data sets. This extraction approach maintains data integrity for change detection by ensuring all changes are captured and transmitted, while significantly reducing the total data volume that consumes bandwidth resources.
4Loss of information
If all point cloud data is processed, then comprehensive analysis is achieved, but computational efficiency decreases
Solution Approach 1:
The patent extracts only the changed portions from point clouds for processing. By focusing computational resources on analyzing only the differences rather than processing complete point clouds, the system achieves comprehensive change analysis while significantly improving processing speed and reducing computational overhead.
Solution Approach 2:
The patent applies partial action by processing only the necessary changed portions rather than complete point clouds. This selective processing approach performs sufficient analysis for change detection purposes without the excessive computation required for full point cloud processing, optimizing the balance between analysis completeness and processing efficiency.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach reduces the amount of data needed to be stored and transmitted by eliminating duplicate data, improving computational efficiency and enabling more complete and interactive point cloud management.
Implementation Method 1
A TOF laser scanner is a scanner in which the distance to a target point is determined based on the speed of light in air between the scanner and a target point
Implementation Method 2
A laser scanner optically scans and measures objects in a volume around the scanner through the acquisition of data points representing object surfaces within the volume. Such data points are obtained by transmitting a beam of light onto the objects and collecting the reflected or scattered light
Data Source
AI summary
Examples described herein provide a method that is performed by a processing system. The method includes receiving a first point cloud comprising a first set of points associated with an environment. The first point cloud is organized into a plurality of segments. A second point cloud is received comprising a second set of points associated with the environment. The second point cloud is aligned with at least one of the plurality of segments. A change is identified between at least a portion of the first set of points within the at least one plurality of segments and a corresponding second set of points in the second point cloud. A third point cloud is generated based on the first point cloud, the second point cloud and the change.