Advertisement

Automatic Normal Orientation in Point Clouds of Building Interiors

  • Sebastian OchmannEmail author
  • Reinhard Klein
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11542)

Abstract

Correct and consistent normal orientation is a fundamental problem in geometry processing. Applications such as feature detection and geometry reconstruction often rely on correctly oriented normals. Many existing approaches make severe assumptions on the input data or the topology of the underlying object which are not applicable to measurements of urban scenes. In contrast, our approach is specifically tailored to the challenging case of unstructured indoor point cloud scans of multi-story, multi-room buildings. We evaluate the correctness and speed of our approach on multiple real-world point cloud datasets.

Keywords

Point clouds Normal orientation 

Notes

Acknowledgments

This work was supported by the DFG projects KL 1142/11-1 (DFG Research Unit FOR 2535 Anticipating Human Behavior) and KL 1142/9-2 (DFG Research Unit FOR 1505 Mapping on Demand).

References

  1. 1.
    Alliez, P., Cohen-Steiner, D., Tong, Y., Desbrun, M.: Voronoi-based variational reconstruction of unoriented point sets. In: Symposium on Geometry Processing, vol. 7, pp. 39–48 (2007)Google Scholar
  2. 2.
    Borodin, P., Zachmann, G., Klein, R.: Consistent normal orientation for polygonal meshes. In: 2004 Computer Graphics International, pp. 18–25. IEEE (2004)Google Scholar
  3. 3.
    Hoppe, H., DeRose, T., Duchamp, T., McDonald, J., Stuetzle, W.: Surface reconstruction from unorganized points, vol. 26. ACM (1992)Google Scholar
  4. 4.
    König, S., Gumhold, S.: Consistent propagation of normal orientations in point clouds. In: VMV, pp. 83–92 (2009)Google Scholar
  5. 5.
    Mullen, P., De Goes, F., Desbrun, M., Cohen-Steiner, D., Alliez, P.: Signing the unsigned: robust surface reconstruction from raw pointsets. Comput. Graph. Forum 29, 1733–1741 (2010)CrossRefGoogle Scholar
  6. 6.
    Oesau, S., Verdie, Y., Jamin, C., Alliez, P., Lafarge, F., Giraudot, S.: Point set shape detection. In: CGAL User and Reference Manual, 4.12 edn. CGAL Editorial Board (2018). https://doc.cgal.org/4.12/Manual/packages.html#PkgPointSetShapeDetection3Summary
  7. 7.
    Parker, S.G., et al.: OptiX: a general purpose ray tracing engine. ACM Trans. Graph. (TOG) 29(4), 66 (2010)CrossRefGoogle Scholar
  8. 8.
    Schertler, N., Savchynskyy, B., Gumhold, S.: Towards globally optimal normal orientations for large point clouds. Comput. Graph. Forum 36, 197–208 (2017)CrossRefGoogle Scholar
  9. 9.
    Schnabel, R., Wahl, R., Klein, R.: Efficient RANSAC for point-cloud shape detection. Comput. Graph. Forum 26(2), 214–226 (2007)CrossRefGoogle Scholar
  10. 10.
    Takayama, K., Jacobson, A., Kavan, L., Sorkine-Hornung, O.: A simple method for correcting facet orientations in polygon meshes based on ray casting. J. Comput. Graph. Tech. 3(4), 53 (2014)Google Scholar
  11. 11.
    Wang, J., Yang, Z., Chen, F.: A variational model for normal computation of point clouds. Vis. Comput. 28(2), 163–174 (2012)CrossRefGoogle Scholar
  12. 12.
    Xie, H., McDonnell, K.T., Qin, H.: Surface reconstruction of noisy and defective data sets. In: Proceedings of the Conference on Visualization 2004, pp. 259–266. IEEE Computer Society (2004)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Institute of Computer Science IIUniversity of BonnBonnGermany

Personalised recommendations