3D Point Cloud Structural Opening Detection via Voxel Projection
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Solution Overview
Problem
Three-dimensional point clouds with sparse and irregular sampling struggle to accurately detect structural features like openings due to noise and occlusions, limiting their utility in representing surface structures effectively.
Innovation Solution
A method involving the creation of a three-dimensional point cloud map, preprocessing to isolate structural points, rotating to align the ground plane, dividing into voxels, and detecting openings through voxel strips, which enhances the clarity and accuracy of structural feature detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Shape
If three-dimensional point cloud data is used to represent structural surfaces, then the shape and surface representation of the structure is improved, but the detection accuracy of specific structural features like openings deteriorates due to sparse and irregular sampling
Solution Approach 1:
The patent divides the three-dimensional point cloud data into multiple two-dimensional projection views (front, back, left, right, top, bottom). This segmentation transforms the complex 3D detection problem into multiple 2D analysis problems, where each projection provides complementary information about structural features. By analyzing multiple projections, the system overcomes the sparsity and irregular sampling limitations of single 3D point cloud data, improving both surface representation and feature detection accuracy.
2Quantity of substance
If traditional three-dimensional point cloud processing is used, then the overall structure representation is achieved, but the detection of specific features like openings is limited due to noise and occlusions
Solution Approach 1:
The patent merges information from multiple two-dimensional projection views to detect structural features. Each projection contains partial information about the structure, and by combining these projections, the system reconstructs a more complete and reliable representation. This merging process compensates for noise and occlusions in individual views, as features visible in multiple projections can be confidently identified while false positives from noise are filtered out through cross-validation.
Data Source
AI summary
A method for detecting an opening in a structure represented by a three-dimensional point cloud may include the steps of: (1) creating a three-dimensional point cloud map of a scene, the three-dimensional point cloud map including a plurality of points representing a ground plane and the structure upon the ground plane, (2) identifying an absence of points within the plurality of points representing the structure, and (3) determining whether the absence of points represents the opening in the structure.