An Experimental Benchmark for Point Set Coarse Matching

Ferran Roure, Yago Díez, Xavier Lladó, Josep Forest, Tomislav Pribanic, Joaquim Salvi

Abstract

Coarse Matching of point clouds is a fundamental problem in a variety of computer vision applications. While many algorithms have been developed in recent years to address its different aspects, the lack of unified measures and commonly agreed upon data hampers algorithm performances comparison. Additionally, a large number of contributions are tested only with synthetic or processed data. This is a problem as the resulting scenario is somewhat less challenging and does not always conform to practical application conditions. In this paper, we present a new, publicly available database that aims at overcoming the existing problems, provide researchers with a useful tool to compare new contributions to existing ones and represent a step towards standardization. The database contains both processed and unprocessed data with attention to specially challenging datasets. It also includes information on correct solution, presence of noise, overlap percentages and additional information that will allow researchers to focus only on specific parts of the matching pipeline.

References

  1. Aiger, D., Mitra, N. J., and Cohen-Or, D. (2008). 4-points congruent sets for robust pairwise surface registration. In ACM Transactions on Graphics, volume 27, page 85.
  2. Albarelli, A., Rodola, E., and Torsello, A. (2010). A gametheoretic approach to fine surface registration without initial motion estimation. In Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, pages 430-437. IEEE.
  3. Besl, P. J. and McKay, N. D. (1992). A method for registration of 3-d shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14(2):239-256.
  4. Bogo, F., Romero, J., Loper, M., and Black, M. J. (2014). FAUST: Dataset and evaluation for 3D mesh registration. In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Piscataway, NJ, USA. IEEE.
  5. Bronstein, A. e. (2010). Shrec 2010: robust feature detection and description benchmark. Eurographics Workshop on 3D Object Retrieval, 2(5):6.
  6. Bronstein, A. M., Bronstein, M. M., and Kimmel, R. (2008). Numerical geometry of non-rigid shapes. Springer.
  7. Dutagaci, H., Cheung, C. P., and Godil, A. (2012). Evaluation of 3d interest point detection techniques via human-generated ground truth. The Visual Computer, 28(9):901-917.
  8. Gelfand, N., Mitra, N. J., Guibas, L. J., and Pottmann, H. (2005). Robust global registration. In Eurographics Symposium on Geometry Processing, pages 197-206.
  9. Johnson, A. E. (1997). Spin-images: A representation for 3-D surface matching. PhD thesis, Citeseer.
  10. Kim, H. and Hilton, A. (2013). Evaluation of 3d feature descriptors for multi-modal data registration.
  11. Larkins, R. L., Cree, M. J., and Dorrington, A. A. (2012). Verification of multi-view point-cloud registration for spherical harmonic cross-correlation. In Proceedings of the 27th Conference on Image and Vision Computing New Zealand, pages 358-363. ACM.
  12. Manay, S., Hong, B.-W., Yezzi, A., and Soatto, S. (2004). Integral invariant signatures. European Conf. on Computer Vision, pages 87-99.
  13. Mellado, N., Aiger, D., and Mitra, N. J. (2014). Super 4pcs fast global pointcloud registration via smart indexing. In Computer Graphics Forum, volume 33, pages 205- 215. Wiley Online Library.
  14. Mian, A., Bennamoun, M., and Owens, R. (2010). On the repeatability and quality of keypoints for local featurebased 3D object retrieval from cluttered scenes. International Journal of Computer Vision, 89(2):348-361.
  15. Pribanic, T., Diez, Y., Fernandez, S., and Salvi, J. (2013). An efficient method for surface registration. In VISAPP (1), pages 500-503.
  16. Pribanic, T., Mrvos?, S., and Salvi, J. (2010). Efficient multiple phase shift patterns for dense 3d acquisition in structured light scanning. Image and Vision Computing, 28(8):1255-1266.
  17. Rusinkiewicz, S. and Levoy, M. (2001). Efficient variants of the icp algorithm. In IEEE International Conference on 3D Digital Imaging and Modeling, pages 145-152.
  18. Rusu, R. B., Blodow, N., and Beetz, M. (2009). Fast point feature histograms (FPFH) for 3D registration. In Robotics and Automation, 2009. ICRA'09. IEEE International Conference on, pages 3212-3217.
  19. Salti, S., Tombari, F., and Stefano, L. D. (2011). A performance evaluation of 3d keypoint detectors. In IEEE International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission, pages 236-243.
  20. Sun, J., Ovsjanikov, M., and Guibas, L. (2009). A concise and provably informative multi-scale signature based on heat diffusion. In Computer Graphics Forum, volume 28, pages 1383-1392.
  21. Tombari, F., Salti, S., and Di Stefano, L. (2010). Unique signatures of histograms for local surface description. European Conf. on Computer Vision, pages 356-369.
  22. Yu, T.-H., Woodford, O. J., and Cipolla, R. (2013). A performance evaluation of volumetric 3d interest point detectors. International Journal of Computer Vision, pages 1-18.
  23. Zhong, Y. (2009). Intrinsic shape signatures: A shape descriptor for 3d object recognition. In IEEE International Conference on Computer Vision Workshops, pages 689-696.
Download


Paper Citation


in Harvard Style

Roure F., Díez Y., Lladó X., Forest J., Pribanic T. and Salvi J. (2015). An Experimental Benchmark for Point Set Coarse Matching . In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-089-5, pages 679-685. DOI: 10.5220/0005361306790685


in Bibtex Style

@conference{visapp15,
author={Ferran Roure and Yago Díez and Xavier Lladó and Josep Forest and Tomislav Pribanic and Joaquim Salvi},
title={An Experimental Benchmark for Point Set Coarse Matching},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={679-685},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005361306790685},
isbn={978-989-758-089-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015)
TI - An Experimental Benchmark for Point Set Coarse Matching
SN - 978-989-758-089-5
AU - Roure F.
AU - Díez Y.
AU - Lladó X.
AU - Forest J.
AU - Pribanic T.
AU - Salvi J.
PY - 2015
SP - 679
EP - 685
DO - 10.5220/0005361306790685