Shape Detection in Point Clouds

Universität Bonn, Technical Report number CG-2006-2, Jan. 2006
 

Abstract

In this work 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 and cones. For models with surfaces composed of these basic shapes only, e.g. 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.

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Bibtex

@TECHREPORT{cg-2006-2,
       author = {Schnabel, Ruwen and Wahl, Roland and Klein, Reinhard},
        title = {Shape Detection in Point Clouds},
       number = {CG-2006-2},
         year = {2006},
        month = jan,
  institution = {Universit{\"a}t Bonn},
     abstract = {In this work 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 and cones. For models
                 with surfaces composed of these basic shapes only, e.g. 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.},
         issn = {1610-8892}
}