3D Research

, 2:3 | Cite as

The 3D Hough Transform for plane detection in point clouds: A review and a new accumulator design

  • Dorit Borrmann
  • Jan ElsebergEmail author
  • Kai LingemannEmail author
  • Andreas NüchterEmail author
3DR Express


The Hough Transform is a well-known method for detecting parameterized objects. It is the de facto standard for detecting lines and circles in 2-dimensional data sets. For 3D it has attained little attention so far. Even for the 2D case high computational costs have lead to the development of numerous variations for the Hough Transform. In this article we evaluate different variants of the Hough Transform with respect to their applicability to detect planes in 3D point clouds reliably. Apart from computational costs, the main problem is the representation of the accumulator. Usual implementations favor geometrical objects with certain parameters due to uneven sampling of the parameter space. We present a novel approach to design the accumulator focusing on achieving the same size for each cell and compare it to existing designs.


Hough Transform 3D laser scans plane detection indoor mapping 


  1. 1.
    M. A. Fischler, R. C. Bolles (1981) Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography, Communications of the ACM. 24:381–395MathSciNetCrossRefGoogle Scholar
  2. 2.
    R. Schnabel, R. Wahl, R. Klein (2007) Efficient RANSAC for Point-Cloud Shape Detection, Computer Graphics Forum.Google Scholar
  3. 3.
    U. Bauer, K. Polthier (2008) Detection of Planar Regions in Volume Data for Topology Optimization, Lecture Notes in Computer Science.Google Scholar
  4. 4.
    J. Poppinga, N. Vaskevicius, A. Birk, K. Pathak (2008) Fast Plane Detection and Polygonalization in noisy 3D Range Images, In IROS’ 08.Google Scholar
  5. 5.
    R. Lakaemper, L. J. Latecki (2006) Extended EM for Planar Approximation of 3D Data, In IEEE International Conference on Robotics and Automation (ICRA’ 06).Google Scholar
  6. 6.
    O. Wulf, K. O. Arras, H. I. Christensen, B. A. Wagner (2004) 2D Mapping of Cluttered Indoor.Google Scholar
  7. 7.
    G. Yu, M. Grossberg, G. Wolberg, I. Stamos (2008) Think Globally, Cluster Locally: A Unified Framework for Range Segmentation, In Proceedings of the 4th International Symposium on 3D Data Processing, Visualization and Transmission (3DPVT’ 08), Atlanta, USA.Google Scholar
  8. 8.
    M. Attene, B. Falcidieno, M. Spagnuolo (2006) Hierarchical mesh segmentation based on fitting primitives, The Visual Computer. 22:181–193CrossRefGoogle Scholar
  9. 9.
    P. V. C. Hough (1962) Method and Means for Recognizing Complex Patterns, US Patent 3069654.Google Scholar
  10. 10.
    R. O. Duda, P. E. Hart (1971) Use of the Hough Transformation to Detect Lines and Curves in Pictures, Technical Note 36, Artificial Intelligence Center, SRI International.Google Scholar
  11. 11.
    N. Kiryati, Y. Eldar, A. M. Bruckstein (1991) A Probabilistic Hough Transform, Pattern Recognition. 24(4):303–316MathSciNetCrossRefGoogle Scholar
  12. 12.
    J. Illingworth, J. Kittler (1988) A Survey on the Hough Transform, Computer Vision, Graphics, and Image Processing. 44:87–116CrossRefGoogle Scholar
  13. 13.
    H. Kälviäinen, P. Hirvonen, L. Xu, E. Oja (1995) Probabilistic and Non-Probabilistic Hough Transforms: Overview and Comparisons, Image and Vision Computing. 13(4)Google Scholar
  14. 14.
    A. Ylä-Jääski, N. Kiryati (1994) Adaptive Termination of Voting in the Probabilistic Circular Hough Transform, IEEE Transactions on Pattern Analysis and Machine Intelligence. 16(9)Google Scholar
  15. 15.
    J. Matas, C. Galambos, J. Kittler (1998) Progressive Probabilistic Hough Transform, In Proceedings of the British Machine Vision Conference. 1:256–265Google Scholar
  16. 16.
    L. Xu, E. Oja, P. Kultanen (1990) A new Curve DetectionMethod: Randomized Hough Transform (RHT), Pattern Recognition Letters. 11:331–338CrossRefzbMATHGoogle Scholar
  17. 17.
    A. Censi, S. Carpin (2009) HSM3D: Feature-Less Global 6DOF Scan-Matching in the Hough/Radon Domain, In Proceedings of the IEEE International Conference on Robotics and Automation.Google Scholar
  18. 18.
    T. Zaharia, F. Preteux (2002) Shape-based Retrieval of 3D Mesh Models, In Proceedings of the IEEE International Conference on Multimedia and Expo.Google Scholar
  19. 19.
    D. Borrmann, J. Elseberg (2009) Deforming Scans for Improving the Map Quality Using Plane Extraction and Thin Plate Splines, Master thesis, University of Osnabrück, Germany.Google Scholar
  20. 20.
    F. Tarsha-Kurdi, T. Landes, P. Grussenmeyer (2007) Hough-transform and extended RANSAC algorithms for automatic detection of 3d building roof planes from lidar data, IAPRS. 36:Part 3 / W52Google Scholar
  21. 21.
    M. B. Dillencourt, H. Samet, T. Tamminen (1992) A general approach to connected-component labeling for arbitrary image representations, J. ACM. 39:253–280MathSciNetCrossRefzbMATHGoogle Scholar
  22. 22.
    Environments by Means of 3D Perception, In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA’ 04). New Orleans, USA. 4204–4209Google Scholar

Copyright information

© 3D Display Research Center and Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.Jacobs University Bremen gGmbHBremenGermany
  2. 2.University of OsnabrückOsnabrückGermany

Personalised recommendations