Skip to main content
SpringerLink
Log in

Aligning Point Clouds with an Effective Local Feature Descriptor

  • Conference paper
  • First Online:
Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health (CyberDI 2019, CyberLife 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1138))

Abstract

Point cloud registration is a crucial step and gaining more importance in many challenging 3D computer vision tasks including 3D reconstruction, autonomous navigation, 3D object recognition and remote sensing. In this work, we proposed a highly discriminative local feature descriptor named Local Point Feature Histogram (LPFH) for 3D point cloud registration. LPFH formulates a simple and comprehensive histogram for surface representation, which encompassed a 3D descriptor. Based on the proposed LPFH, we use Random Sample Consensus (RANSAC) algorithm to form our coarse registration stage, followed by an Iterative Closest Point (ICP) fine registration stage, these two steps form our registration algorithm. Validations and comparisons with other point cloud registration algorithms showed that LPFH is low-dimension, efficient, effective and easy to compute.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Besl, P.J., McKay, N.D.: Method for registration of 3D shapes. In: Sensor Fusion IV: Control Paradigms and Data Structures, vol. 1611. International Society for Optics and Photonics (1992)

    Google Scholar 

  2. Johnson, A.E., Martial, H.: Surface matching for object recognition in complex three-dimensional scenes. Image Vis. Comput. 16(9–10), 635–651 (1998)

    Article  Google Scholar 

  3. Rusu, R.B., et al.: Aligning point cloud views using persistent feature histograms. In: IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE (2008)

    Google Scholar 

  4. Rusu, R.B., Nico, B., Michael, B.: Fast point feature histograms (FPFH) for 3D registration. In: IEEE International Conference on Robotics and Automation. IEEE (2009)

    Google Scholar 

  5. Guo, Y., et al.: Rotational projection statistics for 3D local surface description and object recognition. Int. J. Comput. Vis. 105(1), 63–86 (2013)

    Article  MathSciNet  Google Scholar 

  6. Tombari, F., Salti, S., Di Stefano, L.: Unique signatures of histograms for local surface description. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6313, pp. 356–369. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15558-1_26

    Chapter  Google Scholar 

  7. Chen, Y., Medioni, G.: Object modelling by registration of multiple range images. Image Vis. Comput. 10(3), 145–155 (1992)

    Article  Google Scholar 

  8. Zhang, Z.: Iterative point matching for registration of free-form curves. IRA Rapports de Recherche, Programme 4: Robotique, Image et Vision, no. 1658 (1992)

    Google Scholar 

  9. Gelfand, N.: Feature analysis and registration of scanned surfaces. Ph.D. thesis. Stanford University, Stanford, CA, USA (2007)

    Google Scholar 

  10. Wu, Y.F., et al.: A new method for registration of 3D point sets with low overlapping ratios. Procedia CIRP 27, 202–206 (2015)

    Article  Google Scholar 

  11. Simon, D.A.: Fast and Accurate Shape-Based Registration. Carnegie Mellon University, Pittsburgh (1996)

    Google Scholar 

  12. Rusinkiewicz, S., Levoy, M.: Proceedings of the Third International Conference on 3D Digital Imaging and Modeling, pp. 145–152 (2001)

    Google Scholar 

  13. Sharp, G.C., Lee, S.W., Wehe, D.K.: ICP registration using invariant features. IEEE Trans. Pattern Anal. Mach. Intell. 24(1), 90–102 (2002)

    Article  Google Scholar 

  14. Bae, K.-H.: Evaluation of the convergence region of an automated registration method for 3D laser scanner point clouds. Sensors 9(1), 355–375 (2009)

    Article  Google Scholar 

  15. Johnson, A.E., Hebert, M.: Using spin images for efficient object recognition in cluttered 3D scenes. IEEE Trans. Pattern Anal. Mach. Intell. 21(5), 433–449 (1999)

    Article  Google Scholar 

  16. Rusu, R.B., et al.: Persistent point feature histograms for 3D point clouds. In: Proceedings of the 10th International Conference on Intel Autonomous System (IAS-2010), Baden-Baden, Germany (2008)

    Google Scholar 

  17. Tombari, F., Salti, S., Di Stefano, L.: A combined texture-shape descriptor for enhanced 3D feature matching. In: 18th IEEE International Conference on Image Processing. IEEE (2011)

    Google Scholar 

  18. Muja, M., et al.: Rein-a fast, robust, scalable recognition infrastructure. In: IEEE International Conference on Robotics and Automation. IEEE (2011)

    Google Scholar 

  19. Rusu, R.B., et al.: Fast 3D recognition and pose using the viewpoint feature histogram. In: IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE (2010)

    Google Scholar 

  20. Aldoma, A., et al.: CAD-model recognition and 6DOF pose estimation using 3D cues. In: IEEE International Conference on Computer Vision Workshops (ICCV Workshops). IEEE (2011)

    Google Scholar 

  21. Yang, J., Cao, Z., Zhang, Q.: A fast and robust local descriptor for 3D point cloud registration. Inf. Sci. 346, 163–179 (2016)

    Article  Google Scholar 

  22. Crosilla, F., Visintini, D., Sepic, F.: Reliable automatic classification and segmentation of laser point clouds by statistical analysis of surface curvature values. Appl. Geomatics 1(1–2), 17–30 (2009)

    Article  Google Scholar 

  23. Malassiotis, S., Strintzis, M.G.: Snapshots: a novel local surface descriptor and matching algorithm for robust 3D surface alignment. IEEE Trans. Pattern Anal. Mach. Intell. 29(7), 1285–1290 (2007)

    Article  Google Scholar 

  24. Rusu, R.B., et al.: Towards 3D point cloud based object maps for household environments. Robot. Auton. Syst. 56(11), 927–941 (2008)

    Article  Google Scholar 

  25. Fischler, M.A., Bolles, R.C.: A paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981). Reprinted in readings in computer vision, Fischler, M.A. (ed.)

    Article  Google Scholar 

  26. Fitzgibbon, A.W.: Robust registration of 2D and 3D point sets. Image Vis. Comput. 21(13-14), 1145–1153 (2003)

    Article  Google Scholar 

  27. Rusinkiewicz, S., Marc L.: Efficient variants of the ICP algorithm. In: 3 DIM, vol. 1 (2001)

    Google Scholar 

  28. Zhang, J.: Multi-source remote sensing data fusion: status and trends. Int. J. Image Data Fusion 1(1), 5–24 (2010)

    Article  Google Scholar 

  29. Min, Z., Wang, J., Meng, M.Q.H.: Robust generalized point cloud registration using hybrid mixture model. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 4812–4818. IEEE (2018)

    Google Scholar 

  30. Tong, L., Ying, X.: 3D point cloud initial registration using surface curvature and SURF matching. 3D Res. 9(3), 41 (2010)

    Article  Google Scholar 

Download references

Acknowledgement

In this work, we like to acknowledge the Stanford 3D Scanning Repository for making their datasets available to us. And all the developers of Point Cloud Library for developing some general algorithms for us. This work is supported by Shanghai Science and Technology Commission’s Scientific Research Program (#17DZ1204903).

Author information

Authors and Affiliations

  1. School of Software Engineering, Tongji University, Shanghai, China

    Xialing Feng, Tianran Tan, Yizhe Yuan & Changqing Yin

Authors
  1. Xialing Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tianran Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yizhe Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Changqing Yin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Changqing Yin .

Editor information

Editors and Affiliations

  1. University of Science and Technology, Beijing, China

    Huansheng Ning

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Feng, X., Tan, T., Yuan, Y., Yin, C. (2019). Aligning Point Clouds with an Effective Local Feature Descriptor. In: Ning, H. (eds) Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health. CyberDI CyberLife 2019 2019. Communications in Computer and Information Science, vol 1138. Springer, Singapore. https://doi.org/10.1007/978-981-15-1925-3_18

Download citation

  • DOI: https://doi.org/10.1007/978-981-15-1925-3_18

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-1924-6

  • Online ISBN: 978-981-15-1925-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation