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Semantically Aware Urban 3D Reconstruction with Plane-Based Regularization

  • Thomas HolzmannEmail author
  • Michael Maurer
  • Friedrich Fraundorfer
  • Horst Bischof
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11218)

Abstract

We propose a method for urban 3D reconstruction, which incorporates semantic information and plane priors within the reconstruction process in order to generate visually appealing 3D models. We introduce a plane detection algorithm using 3D lines, which detects a more complete and less spurious plane set compared to point-based methods in urban environments. Further, the proposed normalized visibility-based energy formulation eases the combination of several energy terms within a tetrahedra occupancy labeling algorithm and, hence, is well suited for combining it with class specific smoothness terms. As a result, we produce visually appealing and detailed building models (i.e., straight edges and planar surfaces) and a smooth reconstruction of the surroundings.

Keywords

Plane Detection Algorithm Plane Hypothesis Triple Line Poisson Surface Scene Parts 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

This research was funded by the Austrian Science Fund (FWF) in the project V-MAV (I-1537). We thank Prof. Werner Lienhart and Slaven Kalenjuk from IGMS, TU Graz, Jesus Pestana and Christian Mostegel for providing datasets and Martin R. Oswald for discussion.

Supplementary material

474202_1_En_29_MOESM1_ESM.pdf (24.8 mb)
Supplementary material 1 (pdf 25356 KB)

Supplementary material 2 (mp4 32119 KB)

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Thomas Holzmann
    • 1
    Email author
  • Michael Maurer
    • 1
  • Friedrich Fraundorfer
    • 1
  • Horst Bischof
    • 1
  1. 1.Institute of Computer Graphics and VisionGraz University of TechnologyGrazAustria

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