Point Cloud 3D Model Generation via Plane Segmentation
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Solution Overview
Problem
Existing methods for generating three-dimensional models from point cloud data are inefficient, requiring substantial processing time due to the need to connect and process large numbers of points, and often fail to effectively extract relevant features in a timely manner.
Innovation Solution
A point cloud data processing device comprising a non-plane removing unit, a plane labeling unit, a three-dimensional edge extracting unit, and a two-dimensional edge extracting unit, which segments point cloud data into planes, removes non-plane areas, and integrates three-dimensional and two-dimensional edges to generate a three-dimensional model quickly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a method of connecting adjacent points and forming polygons is used to generate a three-dimensional model from point cloud data, then a complete three-dimensional model can be generated, but enormous amounts of processing time are required
Solution Approach 1:
The patent segments the point cloud data into planar regions using plane fitting algorithms, then processes each plane separately to extract edges. This divides the overwhelming task of processing all points into manageable plane-based units, dramatically reducing computation time while preserving model accuracy
Solution Approach 2:
The patent extracts only the essential edge features from the point cloud data by identifying plane intersections and convex hull boundaries, rather than processing all points to form complete polygons. This extraction approach captures the critical three-dimensional structure without the computational burden of full polygon formation
2Loss of time
If only three-dimensional features are extracted from point cloud data to generate a three-dimensional model, then processing time is reduced, but two-dimensional features within planes are lost
Solution Approach 1:
The patent performs edge extraction in both three-dimensional space (using plane intersections and convex hulls) and two-dimensional space (within each fitted plane). This dual-dimensional approach ensures that both volumetric features and planar features are captured, providing complete feature information while maintaining efficiency
3Measurement precision
If all points in point cloud data are processed to extract edges and features, then feature extraction accuracy is improved, but processing complexity increases
Solution Approach 1:
The patent performs preliminary plane fitting and classification on the point cloud data before edge extraction. By pre-organizing points into planar regions with fitted equations, the subsequent edge extraction process operates on structured data rather than raw points, reducing complexity while maintaining accuracy
Data Source
AI summary
A point cloud data processing device extracts features of an object from point cloud data thereof and automatically generates a three-dimensional model in a short time. The device includes a non-plane removing unit for removing points of non-plane areas from the point cloud data, and a plane labeling unit for adding identical labels to points in the same planes other than the points that are removed by the non-plane removing unit so as to segment the point cloud data into planes. The device also includes a three-dimensional edge extracting unit for extracting three-dimensional edges based on at least one of lines of intersections of the segmented planes and convex lines that convexly cover the segmented planes. The device further includes a two-dimensional edge extracting unit for extracting two-dimensional edges from within the segmented planes, and an edge integrating unit for integrating the three-dimensional edges and the two-dimensional edges.