3D Point Cloud Edge Detection Algorithm
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Solution Overview
Problem
Existing methods for constructing computer models of complex structures from 3D point data are time-consuming and error-prone, especially when dealing with irregular shapes, non-linear edges, and varying point densities, often requiring manual intervention and being sensitive to noise and occlusions.
Innovation Solution
The system allows users to select a seed point along an edge and automatically generates an initial edge profile, which can be adjusted, using algorithms like RANSAC for robust fitting and variable length lookaheads to handle varying point densities, and includes features for user interaction and data processing to model edges accurately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual segmentation and fitting procedures are used to identify edges, then measurement precision can be improved, but loss of time increases significantly
Solution Approach 1:
The system automatically identifies edges by analyzing point cloud data without requiring manual user intervention. The algorithm autonomously processes the 3D scan data, groups points, fits geometric primitives, and identifies edges based on predefined criteria, making the system self-sufficient and eliminating time-consuming manual steps.
Solution Approach 2:
The patent replaces manual mechanical operations (user selecting areas, rotating views, drawing shapes) with automated computational algorithms. The system uses computer-based processing to automatically perform edge detection through point grouping, geometric fitting, and statistical analysis, substituting human expertise with automated computational methods.
2Ease of operation
If step-wise approaches are used to locate edges, then ease of operation is improved, but measurement precision deteriorates due to overshooting or undershooting edge ends
Solution Approach 1:
The system continuously monitors fit statistics during the edge detection process and uses this feedback to dynamically adjust the detection criteria. When fit statistics indicate the edge has been sufficiently captured, the algorithm automatically terminates, preventing both overshooting and undershooting of edge ends while maintaining operational simplicity.
Solution Approach 2:
The patent employs variable detection parameters including adjustable fit thresholds, point grouping criteria, and geometric primitive tolerance levels. These parameters can be modified to optimize the balance between ease of operation and precision, allowing the system to adapt to different edge types and data densities without manual intervention.
3Manufacturing precision
If derivative methods and normal vector calculations are used, then manufacturing precision is improved, but device complexity increases due to computational intensity
Solution Approach 1:
Instead of calculating normal vectors and derivatives for every single point in the cloud, the system selectively processes only the points that form coherent geometric primitives. By grouping points first and then performing calculations only on representative points from each group, the system achieves sufficient precision while dramatically reducing computational complexity.
Solution Approach 2:
The patent extracts and processes only the essential information needed for edge detection, filtering out unnecessary computational operations. By focusing calculations only on points that contribute to geometric primitive formation and edge identification, the system eliminates redundant normal vector calculations and reduces overall computational burden while maintaining manufacturing precision.
Data Source
AI summary
An improved interface and algorithm(s) can be used to simplify and improve the process for locating an edge from a series of points in a point cloud. An interface can allow the user to select a hint point thought to be near an edge of interest, which can be used to generate an initial edge profile. An interface can allow the user to adjust the fit of the initial profile in cross-section, then can use that profile to generate a profile of the entire edge. A moving fit window can use a moving average to extend the edge and determine proper end locations. An interface then can display the results of the fit to the user and allow the user to adjust the fit, such as by adjusting the end points of the calculated edge. Such a process can be used to fit linear or curvilinear edges, and can fit a number of irregular shapes as well as regular shaped such as “v-shaped” edges.