3D Surface Intersection Computation Using Point Cloud Filtering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional methods for computing surface-to-surface intersections in 3D modeling are computationally demanding and slow, especially when dealing with complex surfaces or large datasets, and often introduce approximation errors that fail to capture exact geometric details.
Innovation Solution
A parallel processor-accelerated algorithm that converts surfaces into point clouds, compares points within a defined tolerance, and generates a new point cloud approximating the intersection, while preserving geometric information through parameter space mapping and curve interpolation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to compute surface-to-surface intersections, then geometric accuracy is maintained, but computation time increases significantly and processing speed decreases
Solution Approach 1:
The patent segments the surface intersection problem into multiple manageable components: converting surfaces to point clouds, filtering points by distance threshold, and processing intersections in discrete segments. This segmentation enables parallel processing and reduces the computational complexity of finding intersections between complex surfaces.
Solution Approach 2:
The patent creates a simplified representation (point cloud copy) of the original surfaces by converting them into discrete point sets. This copying approach allows for faster computation while maintaining geometric accuracy, as the point clouds preserve the essential intersection characteristics without requiring complex surface equations.
2Manufacturing precision
If traditional surface intersection methods are applied to complex surfaces or large datasets, then computational demand increases, but processing time becomes excessively long
Solution Approach 1:
The patent performs preliminary actions by converting surfaces to point clouds before computing intersections. This pre-processing step simplifies the data structure and enables faster filtering and comparison operations during the actual intersection computation, significantly reducing processing time while maintaining precision.
Solution Approach 2:
The patent applies local quality by filtering points based on their distance from the second surface, focusing computational effort only on regions where intersections are likely to occur. This selective processing approach maintains high precision for actual intersection points while reducing overall processing time by ignoring non-intersecting regions.
3Measurement precision
If traditional approaches are used for surface intersection computation, then geometric details are captured accurately, but the process becomes slow and inefficient
Solution Approach 1:
The patent replaces traditional mechanical surface equation solving with a point-cloud-based spatial filtering approach. By converting surfaces to point clouds and using distance threshold filtering, the system achieves faster computation speeds while maintaining geometric detail accuracy through the preservation of spatial relationships in the point cloud representation.
Data Source
AI summary
A computer-implemented method for computing an intersection between first and second surfaces of one or more 3D models includes receiving a description for each of the first and second surfaces, using the descriptions to compute one or more intersection curves and outputting the one or more intersection curves.