Hierarchical Hardware/Software Algorithm for Multi-view Object Reconstruction by 3D Point Clouds Matching

  • Ferran Roure
  • Xavier Lladó
  • Joaquim Salvi
  • Tomislav Privanić
  • Yago DiezEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 983)


The Matching or Registration of 3D point clouds is a problem that arises in a variety of research areas with applications ranging from heritage reconstruction to quality control of precision parts in industrial settings. The central problem in this research area is that of receiving two point clouds, usually representing different parts of the same object and finding the best possible rigid alignment between them. Noise in data, a varying degree of overlap and different data acquisition devices make this a complex problem with a high computational cost. This issue is sometimes addressed by adding hardware to the scanning system, but this hardware is frequently expensive and bulky. We present an algorithm that makes use of cheap, widely available (smartphone) sensors to obtain extra information during data acquisition. This information then allows for fast software registration. The first such hybrid hardware-software approach was presented in [31]. In this paper we improve the performance of this algorithm by using hierarchical techniques. Experimental results using real data show how the algorithm presented greatly improves the computation time of the previous algorithm and compares favorably to state of the art algorithms.



We want to thank the authors of the state of the art algorithms considered for making their code publicly available.


  1. 1.
  2. 2.
    Agarwal, P.K., Har-Peled, S., Sharir, M., Wang, Y.: Hausdorff distance under translation for points and balls. In Proceedings of the Nineteenth Annual Symposium on Computational Geometry, SCG 2003, pp. 282–291. ACM, New York (2003)Google Scholar
  3. 3.
    Aiger, D., Mitra, N.J., Cohen-Or, D.: 4-points congruent sets for robust pairwise surface registration. In: SIGGRAPH, vol. 27, no. 3, p. 85 (2008)Google Scholar
  4. 4.
    Andreadis, A., Gregor, R., Sipiran, I., Mavridis, P., Papaioannou, G., Schreck, T.: Fractured 3D object restoration and completion. In: ACM SIGGRAPH 2015 Posters, p. 74. ACM (2015)Google Scholar
  5. 5.
    Arya, S., Mount, D.M.: Approximate nearest neighbor queries in fixed dimensions. In: SODA, vol. 93, pp. 271–280 (1993)Google Scholar
  6. 6.
    Bærentzen, J.A., Gravesen, J., Anton, F., Aanæs, H.: 3D surface registration via iterative closest point (ICP). In: Guide to Computational Geometry Processing, pp. 263–275. Springer, London (2012). Scholar
  7. 7.
    Besl, P.J., McKay, N.D.: A method for registration of 3-D shapes. IEEE Trans. Pattern Anal. Mach. Intell. 14(2), 239–256 (1992)CrossRefGoogle Scholar
  8. 8.
    Buchin, K., Diez, Y., van Diggelen, T., Meulemans, W.: Efficient trajectory queries under the Fréchet distance (GIS Cup). In: Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL 2017, pp. 101:1–101:4. ACM, New York (2017)Google Scholar
  9. 9.
    Choi, S., Kim, S., Chae, J.: Real-time 3D registration using GPU. Mach. Vis. Appl. 22, 837–850 (2011)CrossRefGoogle Scholar
  10. 10.
    Choi, S., Zhou, Q.-Y., Koltun, V.: Robust reconstruction of indoor scenes. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR, pp. 5556–5565. IEEE (2015)Google Scholar
  11. 11.
    da Silva Tavares, J.M.R.: Image processing and analysis: applications and trends. In: 2010 Fifth International Conference on AES-ATEMA# 8217 (2010)Google Scholar
  12. 12.
    Diez, Y., Lopez, M.A., Sellarès, J.A.: Noisy road network matching. In: Cova, T.J., Miller, H.J., Beard, K., Frank, A.U., Goodchild, M.F. (eds.) GIScience 2008. LNCS, vol. 5266, pp. 38–54. Springer, Heidelberg (2008). Scholar
  13. 13.
    Díez, Y., Martí, J., Salvi, J.: Hierarchical normal space sampling to speed up point cloud coarse matching. Pattern Recogn. Lett. 33, 2127–2133 (2012)CrossRefGoogle Scholar
  14. 14.
    Díez, Y., Roure, F., Lladó, X., Salvi, J.: A qualitative review on 3D coarse registration methods. ACM Comput. Surv. (CSUR) 47(3), 45 (2015)CrossRefGoogle Scholar
  15. 15.
    Diez, Y., Sellarès, J.A.: Noisy colored point set matching. Discrete Appl. Math. 159(6), 433–449 (2011)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Elbaz, G., Avraham, T., Fischer, A.: 3D point cloud registration for localization using a deep neural network auto-encoder. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR, pp. 2472–2481 (2017)Google Scholar
  17. 17.
    Fan, J., et al.: Convex hull indexed Gaussian mixture model (CH-GMM) for 3D point set registration. Pattern Recogn. 59, 126–141 (2016)CrossRefGoogle Scholar
  18. 18.
    Gelfand, N., Mitra, N.J., Guibas, L.J., Pottmann, H.: Robust global registration. In: Eurographics Symposium on Geometry Processing, pp. 197–206 (2005)Google Scholar
  19. 19.
    Jerbić, B., Šuligoj, F., Švaco, M., Šekoranja, B.: Robot assisted 3D point cloud object registration. Proc. Eng. 100, 847–852 (2015). 25th DAAAM International Symposium on Intelligent Manufacturing and Automation (2014)CrossRefGoogle Scholar
  20. 20.
    Larkins, R.L., Cree, M.J., Dorrington, A.A.: Verification of multi-view point-cloud registration for spherical harmonic cross-correl. In: 27th Conference on Image Vision Computing, New Zealand, pp. 358–363. ACM (2012)Google Scholar
  21. 21.
    Lian, Z., et al.: A comparison of methods for non-rigid 3D shape retrieval. Pattern Recogn. (2012)Google Scholar
  22. 22.
    Makadia, A., Patterson, A., Daniilidis, K.: Fully automatic registration of 3D point clouds. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 1297–1304 (2006)Google Scholar
  23. 23.
    Manay, S., Hong, B.-W., Yezzi, A.J., Soatto, S.: Integral invariant signatures. In: Pajdla, T., Matas, J. (eds.) ECCV 2004. LNCS, vol. 3024, pp. 87–99. Springer, Heidelberg (2004). Scholar
  24. 24.
    Martins, A., Bessant, M., Manukyan, L., Milinkovitch, M.: R2OBBIE-3D, a fast robotic high-resolution system for quantitative phenotyping of surface geometry and colour-texture. PLoS One 10(6), 1–18 (2015)Google Scholar
  25. 25.
    Matabosch, C., Fofi, D., Salvi, J., Batlle, E.: Registration of surfaces minimizing error propagation for a one-shot multi-slit hand-held scanner. Pattern Recogn. 41(6), 2055–2067 (2008)CrossRefGoogle Scholar
  26. 26.
    Mellado, N., Aiger, D., Mitra, N.J.: Super 4PCS fast global pointcloud registration via smart indexing. In: Computer Graphics Forum, vol. 33, no. 5, pp. 205–215. Wiley Online Library (2014)Google Scholar
  27. 27.
    Mian, A., Bennamoun, M., Owens, R.: On the repeatability and quality of keypoints for local feature-based 3D object retrieval from cluttered scenes. Int. J. Comput. Vis. 89(2), 348–361 (2010)CrossRefGoogle Scholar
  28. 28.
    Oliveira, F.P., Tavares, J.M.R.: Medical image registration: a review. Comput. Methods Biomech. Biomed. Eng. 17(2), 73–93 (2014). PMID: 22435355CrossRefGoogle Scholar
  29. 29.
    Pottmann, H., Wallner, J., Huang, Q.-X., Yang, Y.-L.: Integral invariants for robust geometry processing. Comput. Aided Geom. Des. 26(1), 37–60 (2009)MathSciNetCrossRefGoogle Scholar
  30. 30.
    Pribanić, T., Diez, Y., Fernandez, S., Salvi, J.: An efficient method for surface registration. In: VISAPP, no. 1, pp. 500–503 (2013)Google Scholar
  31. 31.
    Pribanić, T., Diez, Y., Roure, F., Salvi, J.: An efficient surface registration using smartphone. Mach. Vis. Appl. 27(4), 559–576 (2016)CrossRefGoogle Scholar
  32. 32.
    Pribanić, T., Mrvoš, S., Salvi, J.: Efficient multiple phase shift patterns for dense 3D acquisition in structured light scanning. Image Vis. Comput. 28(8), 1255–1266 (2010)CrossRefGoogle Scholar
  33. 33.
    ProjectTango: Project tango (2016). Accessed 20 Sept 2016
  34. 34.
    Roure, F., Díez, Y., Lladó, X., Forest, J., Pribanic, T., Salvi, J.: An experimental benchmark for point set coarse registration. In: International Conference on Computer Vision Theory and Applications (2015)Google Scholar
  35. 35.
    Roure, F., Diez, Y., Lladó, X., Forest, J., Pribanic, T., Salvi, J.: A study on the robustness of shape descriptors to common scanning artifacts. In: 14th International Conference on Machine Vision Applications, MVA, pp. 522–525. IEEE (2015)Google Scholar
  36. 36.
    Roure, F., Llad, X., Salvi, J., Pribanić, T., Diez, Y.: 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, pp. 44–51. INSTICC, SciTePress (2017)Google Scholar
  37. 37.
    Rusinkiewicz, S., Levoy, M.: Efficient variants of the ICP algorithm. In: IEEE International Conference on 3D Digital Imaging and Modeling, pp. 145–152 (2001)Google Scholar
  38. 38.
    Salti, S., Tombari, F., Stefano, L.D.: A performance evaluation of 3D keypoint detectors. In: IEEE International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission, pp. 236–243 (2011)Google Scholar
  39. 39.
    Salvi, J., Matabosch, C., Fofi, D., Forest, J.: A review of recent range image registration methods with accuracy evaluation. Image Vis. Comput. 25(5), 578–596 (2007)CrossRefGoogle Scholar
  40. 40.
    Sipiran, I., Bustos, B.: Harris 3D: a robust extension of the Harris operator for interest point detection on 3D meshes. Vis. Comput. 27(11), 963–976 (2011)CrossRefGoogle Scholar
  41. 41.
    StructureSensor: Structure sensor (2016). Accessed 20 Sept 2016
  42. 42.
    Sun, J., Ovsjanikov, M., Guibas, L.: A concise and provably informative multi-scale signature based on heat diffusion. In: Computer Graphics Forum, vol. 28, pp. 1383–1392 (2009)CrossRefGoogle Scholar
  43. 43.
    Tonioni, A., Salti, S., Tombari, F., Spezialetti, R., Stefano, L.D.: Learning to detect good 3D keypoints. Int. J. Comput. Vis. 126(1), 1–20 (2018)CrossRefGoogle Scholar
  44. 44.
    Yang, J., Li, K., Li, K., Lai, Y.-K.: Sparse non-rigid registration of 3D shapes. In: Computer Graphics Forum, vol. 34, pp. 89–99. Wiley Online Library (2015)Google Scholar
  45. 45.
    Zaharescu, A., Boyer, E., Varanasi, K., Horaud, R.: Surface feature detection and description with applications to mesh matching. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 373–380 (2009)Google Scholar
  46. 46.
    Zhang, J., Sun, J.: Instance-based object recognition in 3D point clouds using discriminative shape primitives. Mach. Vis. Appl. 29(2), 285–297 (2018)MathSciNetCrossRefGoogle Scholar
  47. 47.
    Zou, Y., Wang, X., Zhang, T., Liang, B., Song, J., Liu, H.: BRoPH: an efficient and compact binary descriptor for 3D point clouds. Pattern Recogn. 76, 522–536 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ferran Roure
    • 1
  • Xavier Lladó
    • 2
  • Joaquim Salvi
    • 2
  • Tomislav Privanić
    • 3
  • Yago Diez
    • 4
    Email author
  1. 1.Eurecat, Technology Center of CataloniaBarcelonaSpain
  2. 2.ViCOROB Research InstituteUniversity of GironaGironaSpain
  3. 3.Faculty of Electrical Engineering and ComputingUniversity of ZagrebZagrebCroatia
  4. 4.Faculty of ScienceYamagata UniversityYamagataJapan

Personalised recommendations