3D Point Cloud Region Selection Using Curvature and Color Thresholds
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Solution Overview
Problem
Existing 3D point cloud processing tools lack an efficient method for automatically selecting specific regions of interest in a 3D point cloud, often requiring manual intervention and being prone to selecting irrelevant areas.
Innovation Solution
A computer-implemented method that uses threshold-based criteria, including color differences and normal angle thresholds, to automatically select regions in a 3D point cloud by identifying and expanding from an initial seed point based on neighbor points that meet specific curvature and color criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual selection methods are used for regions in a 3D point cloud, then user control over selection is maintained, but the process is time-consuming and prone to errors
Solution Approach 1:
The system performs automatic region selection by having the algorithm itself identify and select regions based on curvature thresholds and color criteria, eliminating the need for manual user intervention while maintaining high selection accuracy through automated analysis of the point cloud data
Solution Approach 2:
The invention changes the selection process from manual parameter specification to automated parameter-based selection by evaluating curvature values, color differences, and normal angles against predefined thresholds, enabling fast and accurate region selection without user input
2Extent of automation
If existing 3D point cloud processing tools are used, then basic processing functions are available, but automatic region selection is not supported
Solution Approach 1:
The invention segments the point cloud processing into distinct functional modules: curvature calculation, color difference evaluation, normal angle computation, and region selection, allowing each module to be independently optimized while working together to achieve automatic region selection
Solution Approach 2:
The system performs preliminary calculations of curvature, color, and normal properties for all points before the actual selection process, preparing the data in advance so that the final region selection can be executed quickly and efficiently
3Productivity
If threshold-based automatic selection is implemented, then selection speed increases, but selection accuracy may be compromised
Solution Approach 1:
The invention uses multiple parameters (curvature threshold, color difference threshold, normal angle threshold) simultaneously to define selection criteria, allowing the system to maintain high processing speed while achieving accurate region selection through multi-parameter evaluation
Solution Approach 2:
The system evaluates each point against the threshold criteria and uses the results to determine region membership, with the ability to adjust and refine threshold values based on the specific characteristics of the point cloud data being processed
Data Source
AI summary
Automatic selection of region in 3D point cloud is provided. Neighbor points are determined for given seed point of seed points. Responsive to a color difference of a given neighbor point from given seed point being less than neighbor color distance threshold and responsive to an angle between a normal of given neighbor point and a normal of given seed point being less than neighbor normal angle threshold, given neighbor point is added to region in 3D point cloud. Responsive to curvature at given neighbor point being less than curvature threshold, responsive to color difference of given neighbor point from initial seed point being less than initial seed color distance threshold and responsive to an angle between a normal of given neighbor point and a normal of initial seed point being less than an initial seed normal angle, given neighbor point is added to seed points for processing.