Efficient RANSAC for Point-Cloud Shape Detection

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dc.contributor.authorSchnabel, R.en_US
dc.contributor.authorWahl, R.en_US
dc.contributor.authorKlein, R.en_US
dc.date.accessioned2015-02-21T12:41:47Z
dc.date.available2015-02-21T12:41:47Z
dc.date.issued2007en_US
dc.description.abstractIn 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.en_US
dc.description.number2en_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.description.volume26en_US
dc.identifier.doi10.1111/j.1467-8659.2007.01016.xen_US
dc.identifier.issn1467-8659en_US
dc.identifier.pages214-226en_US
dc.identifier.urihttps://doi.org/10.1111/j.1467-8659.2007.01016.xen_US
dc.publisherThe Eurographics Association and Blackwell Publishing Ltden_US
dc.titleEfficient RANSAC for Point-Cloud Shape Detectionen_US
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