3D Point Cloud Plane Extraction via Region Clustering
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Solution Overview
Problem
Existing methods for extracting planes from 3D point clouds are inefficient and time-consuming, particularly when dealing with noisy and redundant data from low-cost depth cameras, which hinders real-time applications in robotics and computer vision.
Innovation Solution
A region-growing-based method that uses agglomerative hierarchical clustering (AHC) on a graph partitioned into disjoint regions or voxels, allowing for concurrent extraction of multiple planes in real time, with the ability to refine plane boundaries for increased accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If RANSAC-based methods are used to extract planes from 3D point clouds, then plane extraction accuracy is improved, but processing time increases significantly
Solution Approach 1:
The patent applies pre-segmentation techniques (Hough transformation or connected component analysis) to divide the point cloud into segments before applying RANSAC. This preliminary organization of data reduces the search space and computational complexity for subsequent plane extraction, enabling real-time processing while maintaining accuracy
Solution Approach 2:
The patent segments the 3D point cloud into multiple regions using Hough transformation or connected component analysis before applying RANSAC to each segment. This segmentation approach reduces the overall computational burden by processing smaller, localized regions rather than the entire point cloud at once
2Measurement precision
If voxel growing method is used to extract planes, then plane extraction is achieved, but processing speed decreases significantly
Solution Approach 1:
The patent performs pre-segmentation of the point cloud into organized regions before applying plane extraction algorithms. This preliminary organization enables faster processing by reducing the data volume that requires intensive computation, addressing the speed limitation of voxel-based methods
3Productivity
If region-growing-based methods are used for plane extraction, then processing speed is improved, but handling of noisy and redundant data becomes more challenging
Solution Approach 1:
The patent extracts and removes inliers (points belonging to identified planes) from the point cloud after each plane extraction step. This iterative removal of processed points reduces redundancy and prevents the same points from being processed multiple times, improving efficiency in noisy environments
4Measurement precision
If multiple planes are extracted iteratively from point clouds, then comprehensive scene modeling is achieved, but computational complexity increases
Solution Approach 1:
The patent applies pre-segmentation techniques to divide the point cloud into meaningful segments before iterative plane extraction. This preliminary organization reduces the computational complexity of each iteration by working with smaller, pre-processed regions rather than the entire point cloud
Solution Approach 2:
The patent segments the point cloud into multiple regions and applies plane extraction to each segment independently. This segmentation strategy reduces overall computational complexity by parallelizing processing across segments and avoiding redundant computations on the same data points
Data Source
AI summary
A method extracts planes from three-dimensional (3D) points by first partitioning the 3D points into disjoint regions. A graph of nodes and edges is then constructed, wherein the nodes represent the regions and the edges represent neighborhood relationships of the regions. Finally, agglomerative hierarchical clustering is applied to the graph to merge regions belonging to the same plane.