3D Point Cloud Structure Detection via Color Filtering
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Solution Overview
Problem
Current methods for detecting deflection vectors in three-dimensional space require high-density point group data and powerful calculation devices, leading to high processing loads and storage needs, with limited capabilities in processing and analyzing such data effectively.
Innovation Solution
A structure detection device that reads three-dimensional point group data including color information, filters data to extract relevant information based on color and frequency, and generates three-dimensional model data to represent detection-target structures, reducing the processing load by selecting only necessary data for analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-density point group data is used to detect deflection vectors, then measurement precision is improved, but processing load and storage requirements increase
Solution Approach 1:
The patent extracts only the necessary point group data for deflection vector detection from the complete three-dimensional laser scanning data. By identifying and extracting only the relevant data points needed for calculating deflection vectors, the system achieves accurate detection without processing the entire high-density point cloud, thereby reducing processing load and storage requirements while maintaining measurement precision.
2Measurement precision
If high-density point group data is used to detect deflection vectors, then measurement precision is improved, but storage requirements increase
Solution Approach 1:
The system extracts only the essential point group data required for deflection vector calculation from the complete three-dimensional scan data. This selective extraction approach stores only the necessary data points rather than the entire high-density point cloud, significantly reducing storage requirements while preserving the accuracy needed for precise deflection vector detection.
3Measurement precision
If complete three-dimensional point group data is processed, then detection accuracy is improved, but processing time increases
Solution Approach 1:
The patent extracts only the relevant point group data necessary for structure detection and deflection vector calculation from the complete three-dimensional scan data. This selective extraction eliminates unnecessary data processing steps, significantly reducing processing time while maintaining detection accuracy by focusing computational resources only on the essential data points.
Solution Approach 2:
The system performs preliminary extraction and selection of relevant point group data before the actual structure detection and deflection vector calculation. By preparing and organizing only the necessary data in advance, the system reduces the computational burden during the main detection process, thereby decreasing overall processing time while preserving detection accuracy.
Data Source
AI summary
A structure detection device according to an embodiment includes: a reading processing unit that reads, as three-dimensional point group data on an object present in a three-dimensional space, data including three-dimensional position information and color information at a point on a surface of the object; a filtering processing unit that performs filtering processing for extracting three-dimensional point group data on a detection-target structure from the three-dimensional point group data on the object present in the three-dimensional space based on the color information; and a generation processing unit that generates three-dimensional model data in which the detection-target structure is represented as a three-dimensional model based on the three-dimensional point group data on the detection-target structure, the three-dimensional point group data being extracted by the filtering processing unit.