Advertisement

Polygonal Reconstruction of Building Interiors from Cluttered Pointclouds

  • Inge CoudronEmail author
  • Steven PuttemansEmail author
  • Toon GoedeméEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11129)

Abstract

In this paper, we propose a framework for reconstructing a compact geometric model from point clouds of building interiors. Geometric reconstruction of indoor scenes is especially challenging due to clutter in the scene, such as furniture and cabinets. The clutter may (partially) hide the structural components of the interior. The proposed framework is able to cope with this clutter by using a hypothesizing and selection strategy, in which candidate faces are firstly generated by intersecting the extracted planar primitives. Secondly, an optimal subset of candidate faces is selected by optimizing a binary labeling problem. We formulate the selection problem as a continuous quadratic optimization problem, allowing us to incorporate a cost function specifically for indoor scenes. The obtained polygonal surface is not only 2-manifold but also oriented, meaning that the surface normals of each polygon are consistently oriented towards the interior. All adjacent and coplanar faces that were selected, are merged into a single face in order to obtain a final geometric model that is as compact as possible. This compact model of the room uses less memory and allows for faster processing when used in virtual reality applications. The method of Nan et al. was used as a starting point for our proposed framework. Finally, as opposed to other state-of-the-art interior modeling approaches, the only input that is required, is the point cloud itself. We do not rely on viewpoint information, nor do we assume constrained input environments with a 2.5D or, more restrictively, a Manhattan-world structure. To demonstrate the practical applicability of our proposed method, we performed various experiments on actual scan data of building interiors.

References

  1. 1.
    Nan, L., Wonka, P.: PolyFit: polygonal surface reconstruction from point clouds. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 2372–2380, October 2017Google Scholar
  2. 2.
    Mura, C., Mattausch, O., Pajarola, R.: Piecewise-planar reconstruction of multi-room interiors with arbitrary wall arrangements. In: Computer Graphics Forum (2016)Google Scholar
  3. 3.
    Turner, E., Zakhor, A.: Floor plan generation and room labeling of indoor environments from laser range data. In: Proceedings of the 9th International Conference on Computer Graphics Theory and Applications: GRAPP, VISIGRAPP 2014, INSTICC, vol. 1, pp. 22–33. SciTePress (2014)Google Scholar
  4. 4.
    Oesau, S., Lafarge, F., Alliez, P.: Indoor scene reconstruction using feature sensitive primitive extraction and graph-cut. ISPRS J. Photogramm. Remote Sens. 90, 68–82 (2014)CrossRefGoogle Scholar
  5. 5.
    Mura, C., Mattausch, O., Villanueva, A.J., Gobbetti, E., Pajarola, R.: Automatic room detection and reconstruction in cluttered indoor environments with complex room layouts. Comput. Graph. 44(C), 20–32 (2014)CrossRefGoogle Scholar
  6. 6.
    Ochmann, S., Vock, R., Wessel, R., Klein, R.: Automatic reconstruction of parametric building models from indoor point clouds. Comput. Graph. 54(C), 94–103 (2016)CrossRefGoogle Scholar
  7. 7.
    Turner, E., Zakhor, A.: Watertight as-built architectural floor plans generated from laser range data. In: 2012 Second International Conference on 3D Imaging, Modeling, Processing, Visualization & Transmission (3DIMPVT), pp. 316–323, October 2012Google Scholar
  8. 8.
    Ikehata, S., Yang, H., Furukawa, Y.: Structured indoor modeling. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 1323–1331, December 2015Google Scholar
  9. 9.
    Murali, S., Speciale, P., Oswald, M.R., Pollefeys, M.: Indoor Scan2BIM: building information models of house interiors. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 6126–6133, September 2017Google Scholar
  10. 10.
    Li, M., Wonka, P., Nan, L.: Manhattan-world urban reconstruction from point clouds. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 54–69. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46493-0_4CrossRefGoogle Scholar
  11. 11.
    Monszpart, A., Mellado, N., Brostow, G.J., Mitra, N.J.: RAPter: rebuilding man-made scenes with regular arrangements of planes. ACM Trans. Graph. 34(4), 103:1–103:12 (2015)CrossRefGoogle Scholar
  12. 12.
    Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Sutherland, I.E., Hodgman, G.W.: Reentrant polygon clipping. Commun. ACM 17(1), 32–42 (1974)CrossRefGoogle Scholar
  14. 14.
    Da, T.K.F.: 2D alpha shapes. In: CGAL User and Reference Manual, 4.12 edn. CGAL Editorial Board (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.EAVISE Research GroupKU LeuvenSint-Katelijne-WaverBelgium

Personalised recommendations