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Topology-Based 3D Reconstruction of Mid-Level Primitives in Man-Made Environments

  • Dominik WoltersEmail author
  • Reinhard Koch
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11269)

Abstract

In this paper a novel reconstruction method is presented that uses the topological relationship of detected image features to create a highly abstract but semantically rich 3D model of the reconstructed scenes. In the first step, a combined image-based reconstruction of points and lines is performed based on the current state of art structure from motion methods. Subsequently, connected planar three-dimensional structures are reconstructed by a novel method that uses the topological relationships between the detected image features. The reconstructed 3D models enable a simple extraction of geometric shapes, such as rectangles, in the scene.

References

  1. 1.
    Agarwal, S., Mierle, K., et al.: Ceres SolverGoogle Scholar
  2. 2.
    Agarwal, S., Snavely, N., Simon, I., Sietz, S.M., Szeliski, R.: Building Rome in a day. In: Twelfth IEEE International Conference on Computer Vision (ICCV 2009). IEEE, Kyoto, September 2009Google Scholar
  3. 3.
    Alahi, A., Ortiz, R., Vandergheynst, P.: FREAK: fast retina keypoint. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 510–517. IEEE, June 2012Google Scholar
  4. 4.
    Budroni, A., Böhm, J.: Automatic 3D modelling of indoor manhattan-world scenes from laser data. In: Proceedings of the International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, pp. 115–120 (2010)Google Scholar
  5. 5.
    Coughlan, J., Yuille, A.: Manhattan world: compass direction from a single image by Bayesian inference. In: Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 2, pp. 941–947. IEEE (1999)Google Scholar
  6. 6.
    Frahm, J.-M., et al.: Building Rome on a cloudless day. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6314, pp. 368–381. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-15561-1_27CrossRefGoogle Scholar
  7. 7.
    Gallup, D., Frahm, J.M., Mordohai, P., Yang, Q., Pollefeys, M.: Real-time plane-sweeping stereo with multiple sweeping directions. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8. IEEE, June 2007Google Scholar
  8. 8.
    Hofer, M., Maurer, M., Bischof, H.: Improving sparse 3D models for man-made environments using line-based 3D reconstruction. In: 2014 2nd International Conference on 3D Vision, vol. 1, pp. 535–542, December 2014Google Scholar
  9. 9.
    Mathias, M., Martinović, A., Van Gool, L.: ATLAS: a three-layered approach to facade parsing. Int. J. Comput. Vis. 118(1), 22–48 (2016)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Mayer, H., Reznik, S.: Building Façade interpretation from image sequences. In: Proceedings of the ISPRS Workshop CMRT, pp. 55–60 (2005)Google Scholar
  11. 11.
    Olufs, S., Vincze, M.: Room-structure estimation in Manhattan-like environments from dense 2 1/2D range data using minumum entropy and histograms. In: 2011 IEEE Workshop on Applications of Computer Vision (WACV), pp. 118–124. IEEE, January 2011Google Scholar
  12. 12.
    Rahmani, K., Mayer, H.: High quality facade segmentation based on structured random forest, region proposal network and rectangular fitting. ISPRS Ann. Photogram. Remote Sens. Spat. Inf. Sci. IV–2, 223–230 (2018)CrossRefGoogle Scholar
  13. 13.
    Schmitz, M., Mayer, H.: A convolutional network for semantic facade segmentation and interpretation. ISPRS - Int. Arch. Photogr. Remote Sens. Spat. Inf. Sci. XLI–B3, 709–715 (2016).  https://doi.org/10.5194/isprsarchives-XLI-B3-709-2016CrossRefGoogle Scholar
  14. 14.
    Sinha, S.N., Steedly, D., Szeliski, R.: Piecewise planar stereo for image-based rendering. In: ICCV, pp. 1881–1888. Citeseer (2009)Google Scholar
  15. 15.
    Snavely, N., Seitz, S.M., Szeliski, R.: Photo tourism: exploring photo collections in 3D. In: ACM Transactions on Graphics (TOG), vol. 25, pp. 835–846. ACM (2006)Google Scholar
  16. 16.
    Werner, T., Zisserman, A.: New techniques for automated architectural reconstruction from photographs. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2351, pp. 541–555. Springer, Heidelberg (2002).  https://doi.org/10.1007/3-540-47967-8_36CrossRefGoogle Scholar
  17. 17.
    Wolters, D.: Automatic 3D reconstruction of indoor manhattan world scenes using kinect depth data. In: Jiang, X., Hornegger, J., Koch, R. (eds.) GCPR 2014. LNCS, vol. 8753, pp. 715–721. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-11752-2_59CrossRefGoogle Scholar
  18. 18.
    Wolters, D., Koch, R.: Combined precise extraction and topology of points, lines and curves in man-made environments. In: Roth, V., Vetter, T. (eds.) GCPR 2017. LNCS, vol. 10496, pp. 115–125. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-66709-6_10CrossRefGoogle Scholar
  19. 19.
    Xu, C., Zhang, L., Cheng, L., Koch, R.: Pose estimation from line correspondences: a complete analysis and a series of solutions. IEEE Trans. Pattern Ana. Mach. Intell. 39(6), 1209–1222 (2017).  https://doi.org/10.1109/TPAMI.2016.2582162CrossRefGoogle Scholar
  20. 20.
    Zhang, L., Koch, R.: An efficient and robust line segment matching approach based on LBD descriptor and pairwise geometric consistency. J. Vis. Commun. Image Represent. 24(7), 794–805 (2013)CrossRefGoogle Scholar
  21. 21.
    Zhang, L., Koch, R.: Structure and motion from line correspondences: representation, projection, initialization and sparse bundle adjustment. J. Vis. Commun. Image Represent. 25(5), 904–915 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceKiel UniversityKielGermany

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