Implicit Shape Models for 3D Shape Classification with a Continuous Voting Space

Viktor Seib, Norman Link, Dietrich Paulus

2015

Abstract

Recently, different adaptations of Implicit Shape Models (ISM) for 3D shape classification have been presented. In this paper we propose a new method with a continuous voting space and keypoint extraction by uniform sampling. We evaluate different sets of typical parameters involved in the ISM algorithm and compare the proposed algorithm on a large public dataset with state of the art approaches.

References

  1. A Benchmark for 3D Mesh Segmentation, Princeton University (2014). Aim@shape watertight dataset (19 of 20 classes). Available at http://segeval.cs.princeton.edu/.
  2. Alexa, M., Behr, J., Cohen-Or, D., Fleishman, S., Levin, D., and Silva, C. T. (2003). Computing and rendering point set surfaces. IEEE Transactions on Visualization and Computer Graphics.
  3. Ballard, D. H. (1981). Generalizing the hough transform to detect arbitrary shapes. Pattern Recognition, 13(2):111-122.
  4. Barequet, G. and Har-Peled, S. (2001). Efficiently approximating the minimum-volume bounding box of a point set in three dimensions. Journal of Algorithms, 38(1):91-109.
  5. Bay, H., Tuytelaars, T., and Gool, L. J. V. (2006). Surf: Speeded up robust features. ECCV, pages 404-417.
  6. Cheng, Y. (1995). Mean Shift, Mode Seeking, and Clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 17(8):790-799.
  7. Fukunaga, K. and Hostetler, L. D. (1975). The Estimation of the Gradient of a Density Function, with Applications in Pattern Recognition. IEEE Transactions on Information Theory, 21(1):32-40.
  8. Har-Peled, S. (2001). A practical approach for computing the diameter of a point set. In Proc. of the 17th Annual Symposium on Computational Geometry, pages 177- 186.
  9. Harris, C. and Stephens, M. (1988). A combined corner and edge detector. In 4th Alvey Vision Conf., pages 147-151.
  10. Hoppe, H., DeRose, T., Duchamp, T., McDonald, J., and Stuetzle, W. (1992). Surface reconstruction from unorganized points. In Proc. of the 19th Annual Conf. on Computer Graphics and Interactive Techniques, pages 71-78.
  11. Hough, P. V. C. and Arbor, A. (1962). Method and means for recognizing complex patterns. Technical Report US Patent 3069 654, US Patent.
  12. Knopp, J., Prasad, M., and Van Gool, L. (2010a). Orientation invariant 3d object classification using hough transform based methods. In Proc. of the ACM Workshop on 3D Object Retrieval, pages 15-20.
  13. Knopp, J., Prasad, M., Willems, G., Timofte, R., and Van Gool, L. (2010b). Hough transform and 3d surf for robust three dimensional classification. In ECCV (6), pages 589-602.
  14. Leibe, B., Leonardis, A., and Schiele, B. (2004). Combined object categorization and segmentation with an implicit shape model. In ECCV' 04 Workshop on Statistical Learning in Computer Vision, pages 17-32.
  15. Leibe, B. and Schiele, B. (2003). Interleaved object categorization and segmentation. In BMVC.
  16. Markley, F. L., Cheng, Y., Crassidis, J. L., and Oshman, Y. (2007). Quaternion averaging. Journal of Guidance Control and Dynamics, 30(4):1193-1197.
  17. Rusu, R. B., Blodow, N., and Beetz, M. (2009). Fast point feature histograms (fpfh) for 3d registration. In Proc. of the Int. Conf. on Robotics and Automation (ICRA), pages 3212-3217. IEEE.
  18. Rusu, R. B., Marton, Z. C., Blodow, N., and Beetz, M. (2008). Persistent point feature histograms for 3d point clouds. In Proc. of the 10th Int. Conf. on Intelligent Autonomous Systems.
  19. Salti, S., Tombari, F., and Di Stefano, L. (2010). On the use of implicit shape models for recognition of object categories in 3d data. In ACCV (3), Lecture Notes in Computer Science, pages 653-666.
  20. SHape REtrieval Contest 2007 (2014). Aim@shape watertight dataset. Available at http://watertight.ge.imati.cnr.it/.
  21. Sipiran, I. and Bustos, B. (2011). Harris 3d: a robust extension of the harris operator for interest point detection on 3d meshes. The Visual Computer, 27(11):963-976.
  22. Tombari, F., Salti, S., and Di Stefano, L. (2010). Unique signatures of histograms for local surface description. In Proc. of the European Conf. on computer vision (ECCV), ECCV'10, pages 356-369. Springer-Verlag.
  23. Wittrowski, J., Ziegler, L., and Swadzba, A. (2013). 3d implicit shape models using ray based hough voting for furniture recognition. In 3DTV-Conference, 2013 Int. Conf. on, pages 366-373. IEEE.
  24. Zhong, Y. (2009). Intrinsic shape signatures: A shape descriptor for 3d object recognition. In 2009 IEEE 12th Int. Conf. on Computer Vision workshops, ICCV, pages 689-696.
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Paper Citation


in Harvard Style

Seib V., Link N. and Paulus D. (2015). Implicit Shape Models for 3D Shape Classification with a Continuous Voting Space . In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-090-1, pages 33-43. DOI: 10.5220/0005290700330043


in Bibtex Style

@conference{visapp15,
author={Viktor Seib and Norman Link and Dietrich Paulus},
title={Implicit Shape Models for 3D Shape Classification with a Continuous Voting Space},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={33-43},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005290700330043},
isbn={978-989-758-090-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015)
TI - Implicit Shape Models for 3D Shape Classification with a Continuous Voting Space
SN - 978-989-758-090-1
AU - Seib V.
AU - Link N.
AU - Paulus D.
PY - 2015
SP - 33
EP - 43
DO - 10.5220/0005290700330043