3D Point Cloud Compression for Facility Inspection
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Solution Overview
Problem
The large volume of 3D point cloud data acquired using 3D laser scanners for facility inspection is costly to store and maintain, and existing compression methods, such as pseudo-converting coordinates into color signals, result in irreversible compression that cannot adjust compression ratios for individual facilities, affecting coordinate accuracy.
Innovation Solution
A data processing device that divides point cloud data by facility type, converts coordinates into color signals, performs image compression with facility-specific ratios, and stores compressed data with associated parameters to maintain coordinate accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If 3D point cloud data is compressed using irreversible compression methods, then data size is reduced, but coordinate accuracy is degraded
Solution Approach 1:
The patent segments the 3D point cloud data by dividing it into multiple datasets based on spatial regions or object categories. Each segment is then compressed independently with optimized parameters, allowing different compression ratios for different regions while maintaining overall coordinate accuracy for critical measurements.
Solution Approach 2:
The patent applies local quality by assigning different compression ratios to different regions or types of point cloud data. High-accuracy regions (such as facility areas requiring precise measurement) maintain higher quality compression, while less critical regions use higher compression ratios, thus balancing data size reduction with measurement precision requirements.
2Quantity of substance
If compression ratio is increased to reduce data size, then storage cost is reduced, but coordinate positions change significantly
Solution Approach 1:
The patent implements dynamics by making the compression ratio adjustable and adaptable rather than fixed. The system can dynamically select appropriate compression ratios based on the specific requirements of different facilities or regions, allowing optimization between data size and coordinate accuracy for each individual case.
Solution Approach 2:
The patent changes the compression parameter (compression ratio) as a variable that can be adjusted according to specific needs. By modifying this parameter dynamically for different datasets or regions, the system achieves optimal balance between reducing data size and maintaining coordinate position accuracy for facility inspection purposes.
3Device complexity
If entire 3D point cloud data is compressed at once, then processing is simplified, but compression ratio cannot be adjusted for each facility
Solution Approach 1:
The patent segments the large 3D point cloud data into smaller, manageable datasets that can be processed independently. This segmentation allows the system to apply different compression ratios to different facilities or regions while maintaining relatively simple processing procedures for each individual segment, thus achieving both simplicity and adaptability.
Data Source
AI summary
An object of the present invention is to provide a data processing device, a data processing method, and a program capable of compressing 3D point cloud data while maintaining the accuracy of restored coordinates to the extent that it can be used for a communication facility inspection technique. A data processing device according to the present invention divides measured point cloud data for each facility on the basis of a identification function, generates an image file in which each coordinate value is regarded as each color signal for each of the divided point cloud data, performs image compression processing on the image file with a compression ratio determined for each facility, and stores the image file subjected to the image compression processing and the parameters used for the image compression processing in association with each other.