Semantically Aware Urban 3D Reconstruction with Plane-Based Regularization
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.
Cite
Text
Holzmann et al. "Semantically Aware Urban 3D Reconstruction with Plane-Based Regularization." Proceedings of the European Conference on Computer Vision (ECCV), 2018. doi:10.1007/978-3-030-01264-9_29Markdown
[Holzmann et al. "Semantically Aware Urban 3D Reconstruction with Plane-Based Regularization." Proceedings of the European Conference on Computer Vision (ECCV), 2018.](https://mlanthology.org/eccv/2018/holzmann2018eccv-semantically/) doi:10.1007/978-3-030-01264-9_29BibTeX
@inproceedings{holzmann2018eccv-semantically,
title = {{Semantically Aware Urban 3D Reconstruction with Plane-Based Regularization}},
author = {Holzmann, Thomas and Maurer, Michael and Fraundorfer, Friedrich and Bischof, Horst},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
year = {2018},
doi = {10.1007/978-3-030-01264-9_29},
url = {https://mlanthology.org/eccv/2018/holzmann2018eccv-semantically/}
}