Efficient RANSAC for Point-Cloud Shape Detection
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Date
2007
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Publisher
The Eurographics Association and Blackwell Publishing Ltd
Abstract
In this paper we present an automatic algorithm to detect basic shapes in unorganized point clouds. The algorithm decomposes the point cloud into a concise, hybrid structure of inherent shapes and a set of remaining points. Each detected shape serves as a proxy for a set of corresponding points. Our method is based on random sampling and detects planes, spheres, cylinders, cones and tori. For models with surfaces composed of these basic shapes only, for example, CAD models, we automatically obtain a representation solely consisting of shape proxies. We demonstrate that the algorithm is robust even in the presence of many outliers and a high degree of noise. The proposed method scales well with respect to the size of the input point cloud and the number and size of the shapes within the data. Even point sets with several millions of samples are robustly decomposed within less than a minute. Moreover, the algorithm is conceptually simple and easy to implement. Application areas include measurement of physical parameters, scan registration, surface compression, hybrid rendering, shape classification, meshing, simplification, approximation and reverse engineering.
Description
@article{10.1111:j.1467-8659.2007.01016.x,
journal = {Computer Graphics Forum},
title = {{Efficient RANSAC for Point-Cloud Shape Detection}},
author = {Schnabel, R. and Wahl, R. and Klein, R.},
year = {2007},
publisher = {The Eurographics Association and Blackwell Publishing Ltd},
ISSN = {1467-8659},
DOI = {10.1111/j.1467-8659.2007.01016.x}
}