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Machine Vision and Applications

, Volume 25, Issue 7, pp 1877–1891 | Cite as

3D Hough transform for sphere recognition on point clouds

A systematic study and a new method proposal
  • Marco Camurri
  • Roberto Vezzani
  • Rita Cucchiara
Original Paper

Abstract

Three-dimensional object recognition on range data and 3D point clouds is becoming more important nowadays. Since many real objects have a shape that could be approximated by simple primitives, robust pattern recognition can be used to search for primitive models. For example, the Hough transform is a well-known technique which is largely adopted in 2D image space. In this paper, we systematically analyze different probabilistic/randomized Hough transform algorithms for spherical object detection in dense point clouds. In particular, we study and compare four variants which are characterized by the number of points drawn together for surface computation into the parametric space and we formally discuss their models. We also propose a new method that combines the advantages of both single-point and multi-point approaches for a faster and more accurate detection. The methods are tested on synthetic and real datasets.

Keywords

3D Hough transform Sphere detection Randomized HT Probabilistic HT 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Marco Camurri
    • 2
  • Roberto Vezzani
    • 1
  • Rita Cucchiara
    • 1
  1. 1.University of Modena and Reggio EmiliaModenaItaly
  2. 2.Department of Advanced RoboticsIstituto Italiano di TecnologiaGenoaItaly

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