Hierarchical Techniques to Improve Hybrid Point Cloud Registration

Ferran Roure, Xavier Lladó, Joaquim Salvi, Tomislav Pribanić, Yago Diez

Abstract

Reconstructing 3D objects by gathering information from multiple spatial viewpoints is a fundamental problem in a variety of applications ranging from heritage reconstruction to industrial image processing. A central issue is known as the ”point set registration or matching” problem. where the two sets being considered are to be rigidly aligned. This is a complex problem with a huge search space that suffers from high computational costs or requires expensive and bulky hardware to be added to the scanning system. To address these issues, a hybrid hardware-software approach was presented in (Pribanic et al., 2016) allowing for fast software registration by using commonly available (smartphone) sensors. In this paper we present hierarchical techniques to improve the performance of this algorithm. Additionally, we compare the performance of our algorithm against other approaches. Experimental results using real data show how the algorithm presented greatly improves the time of the previous algorithm and perform best over all studied algorithms.

References

  1. Agarwal, P. K., Har-Peled, S., Sharir, M., and Wang, Y. (2003). Hausdorff distance under translation for points and balls. In Proceedings of the Nineteenth Annual Symposium on Computational Geometry, SCG 7803, pages 282-291, New York, NY, USA. ACM.
  2. Aiger, D., Mitra, N. J., and Cohen-Or, D. (2008). 4-points congruent sets for robust pairwise surface registration. In SIGGRAPH, volume 27(3), page 85.
  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. Choi, S., Kim, S., and Chae, J. (2011). Real-time 3d registration using gpu. Machine Vision and Applications, pages 837-850.
  5. da Silva Tavares, J. M. R. (2010). Image processing and analysis: applications and trends. In AES-ATEMA# 8217; 2010 Fifth International Conference.
  6. Diez, Y., Lopez, M. A., and Sellares, J. A. (2008). Noisy road network matching. In International Conference on Geographic Information Science, pages 38-54. Springer Berlin Heidelberg.
  7. Díez, Y., Mart í, J., and Salvi, J. (2012). Hierarchical normal space sampling to speed up point cloud coarse matching. Pattern Recognition Letters, 33:2127 - 2133.
  8. Díez, Y., Roure, F., Llad ó, X., and Salvi, J. (2015). A qualitative review on 3d coarse registration methods. ACM Computing Surveys (CSUR), 47(3):45.
  9. Diez, Y. and Sellarès, J. A. (2011). Noisy colored point set matching. Discrete Applied Mathematics, 159(6):433-449.
  10. Larkins, R. L., Cree, M. J., and Dorrington, A. A. (2012). Verification of multi-view point-cloud registration for spherical harmonic cross-correl. In 27th Conf. Image Vision Comp. New Zealand, pages 358-363. ACM.
  11. Makadia, A., Patterson, A., and Daniilidis, K. (2006). Fully automatic registration of 3d point clouds. In Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on, volume 1, pages 1297-1304.
  12. Martins, A., Bessant, M., Manukyan, L., and Milinkovitch, M. (2015). R2obbie-3d, a fast robotic high-res. system for quant. phenotyping of surf. geom. and colourtexture. PLoS ONE, 10(6):1-18.
  13. Matabosch, C., Fofi, D., Salvi, J., and Batlle, E. (2008). Registration of surfaces minimizing error propagation for a one-shot multi-slit hand-held scanner. Pattern Recognition, 41(6):2055 - 2067.
  14. Mellado, N., Aiger, D., and Mitra, N. J. (2014). Super 4pcs fast global pointcloud registration via smart indexing. In Computer Graphics Forum, volume 33(5), pages 205-215. Wiley Online Library.
  15. 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.
  16. Oliveira, F. P. and Tavares, J. M. R. (2014). Medical image registration: a review. Computer Methods in Biomechanics and Biomedical Engineering, 17(2):73-93. PMID: 22435355.
  17. Pribanic, T., Diez, Y., Fernandez, S., and Salvi, J. (2013). An efficient method for surface registration. In VISAPP (1), pages 500-503.
  18. Pribanic, T., Diez, Y., Roure, F., and Salvi, J. (2016). An efficient surface registration using smartphone. Machine Vision and Applications, 27(4):559-576.
  19. 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.
  20. ProjectTango (2016). Project tango. https://www.google. com/atap/projecttango/#project. Accessed: 2016-09- 20.
  21. Roure, F., Díez, Y., Llad ó, X., Forest, J., Pribanic, T., and Salvi, J. (2015a). An experimental benchmark for point set coarse registration. In Int. Conf. on Computer Vision Theory and Applications.
  22. Roure, F., Diez, Y., Llad ó, X., Forest, J., Pribanic, T., and Salvi, J. (2015b). A study on the robustness of shape descriptors to common scanning artifacts. In 14th Int. Conf. Mach. Vis. App., MVA., pages 522-525. IEEE.
  23. 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.
  24. StructureSensor (2016). Structure sensor. http://structure. io/. Accessed: 2016-09-20.
Download


Paper Citation


in Harvard Style

Roure F., Lladó X., Salvi J., Pribanić T. and Diez Y. (2017). Hierarchical Techniques to Improve Hybrid Point Cloud Registration . In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017) ISBN 978-989-758-225-7, pages 44-51. DOI: 10.5220/0006112600440051


in Bibtex Style

@conference{visapp17,
author={Ferran Roure and Xavier Lladó and Joaquim Salvi and Tomislav Pribanić and Yago Diez},
title={Hierarchical Techniques to Improve Hybrid Point Cloud Registration},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017)},
year={2017},
pages={44-51},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006112600440051},
isbn={978-989-758-225-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017)
TI - Hierarchical Techniques to Improve Hybrid Point Cloud Registration
SN - 978-989-758-225-7
AU - Roure F.
AU - Lladó X.
AU - Salvi J.
AU - Pribanić T.
AU - Diez Y.
PY - 2017
SP - 44
EP - 51
DO - 10.5220/0006112600440051