Three-Dimensional Point Cloud Registration Based on Normal Vector Angle

  • Liang Li
  • Xingyan CaoEmail author
  • Jie Sun
Research Article


The widespread use of three-dimensional laser scanning makes point cloud registration essential for model reconstruction. Although iterative closest point (ICP) is a widely used algorithm for automatic and accurate point cloud registration, the ICP algorithm is time-intensive and typically falls in local optima. We employ the normal vector angle of the 3D model and integrate a heuristic search into the ICP algorithm to improve its efficiency, especially while registering complex surfaces. We compare this improved ICP algorithm with the original ICP algorithm and variants of the algorithm in terms of convergence, robustness, and response from a poor initial cloud point position to verify that the proposed improvements outperform the conventional ICP algorithm in the three evaluated aspects when processing point clouds with complex surfaces.


3D laser scanning Point cloud Fine registration Iterative closest point (ICP) 



The authors gratefully acknowledge the financial support from the National Natural Science Foundation of China (Grant No. 51504239).


  1. Besl, P. J., & Mckay, N. D. (1992). Method for registration of 3-D shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14(3), 239–256.CrossRefGoogle Scholar
  2. Chen, Y., & Medioni, G. (1991). Object modeling by registration of multiple range images. In IEEE international conference on robotics and automation, 1991. Proceedings (pp. 145–155). IEEE.Google Scholar
  3. Chen, X., Zhang, G., & Hua, X. (2015). Point cloud simplification based on the information entropy of normal vector angle. Chinese Journal of Lasers, 08, 336–344.Google Scholar
  4. Dorigo, M. (1992). Optimization, learning and natural algorithms. Thesis Politecnico Di Milano Italy.Google Scholar
  5. Fitzgibbon, A. W. (2015). Robust registration of 2D and 3D point sets. Image and Vision Computing, 21(s 13–14), 1145–1153.Google Scholar
  6. Gelfand, N., Rusinkiewicz, S., Ikemoto, L., et al. (2003). Geometrically stable sampling for the ICP algorithm. In International conference on 3-D digital imaging and modeling, 2003. 3DIM 2003. Proceedings (pp. 260–267). IEEE.Google Scholar
  7. Hoppe, H., Derose, T., Duchamp, T., et al. (1999). Surface reconstruction from unorganized points. ACM SIGGRAPH Computer Graphics, 26(2), 71–78.CrossRefGoogle Scholar
  8. Jiang, J., Cheng, J., & Chen, X. (2009). Registration for 3-D point cloud using angular-invariant feature. Neurocomputing, 72(16–18), 3839–3844.CrossRefGoogle Scholar
  9. Jost, T., & Heinz, H. (2003). A multi-resolution ICP with heuristic closest point search for fast and robust 3D registration of range images. In IEEE conference on 3D imaging and modeling (pp. 427–433).Google Scholar
  10. Li, W., & Song, P. (2015). A modified ICP algorithm based on dynamic adjustment factor for registration of point cloud and CAD model. Pattern Recognition Letters, 65, 88–94.CrossRefGoogle Scholar
  11. Mitra, N. J., Gelfand, N., Pottmann, H., et al. (2004). Registration of point cloud data from a geometric optimization perspective. In Proceedings of the 2004 Eurographics/ACM SIGGRAPH symposium on geometry processing (pp. 22–31). ACM.Google Scholar
  12. Moravec, H. P. (1977). Towards automatic visual obstacle avoidance. In International joint conference on artificial intelligence (pp. 584–584).Google Scholar
  13. Pauly, M., Keiser, R., Kobbelt, L. P., et al. (2010). Shape modeling with point-sampled geometry. ACM Transactions on Graphics, 22(3), 641–650.CrossRefGoogle Scholar
  14. Pauly, M., Mitra, N. J., Giesen, J., et al. (2005). Example-based 3D scan completion. In Symposium on geometry processing (pp. 23–32).Google Scholar
  15. Pulli, K. (1999). Multiview registration for large data sets. In International conference on 3-D digital imaging and modeling (pp. 160–168). IEEE Computer Society.Google Scholar
  16. Rusinkiewicz, S., & Levoy, M. (2001). Efficient variants of the ICP algorithm. In 3DIM (p. 145). IEEE Computer Society.Google Scholar
  17. Woo, H., Kang, E., Wang, S., et al. (2002). A new segmentation method for point cloud data. International Journal of Machine Tools and Manufacture, 42(2), 167–178.CrossRefGoogle Scholar
  18. Yang, B., & Zang, Y. (2014). Automated registration of dense terrestrial laser-scanning point clouds using curves. ISPRS Journal of Photogrammetry & Remote Sensing, 95(3), 109–121.CrossRefGoogle Scholar
  19. Yoon, M., Lee, Y., Lee, S., et al. (2007). Surface and normal ensembles for surface reconstruction. Computer-Aided Design, 39(5), 408–420.CrossRefGoogle Scholar
  20. Zeng, F. X., Li, L., & Diao, X. P. (2017). Iterative closest point algorithm registration based on curvature features. Laser & Optoelectronics Progress, 54(1), 107–114. (in Chinese).Google Scholar

Copyright information

© Indian Society of Remote Sensing 2018

Authors and Affiliations

  1. 1.School of Environment Science and Spatial InformaticsChina University of Mining and TechnologyXuzhouPeople’s Republic of China

Personalised recommendations