VASAD: a Volume and Semantic dataset for Building Reconstruction from Point Clouds

Pierre-Alain Langlois  Yang Xiao  Alexandre Boulch  Renaud Marlet

ICPR 2022

Abstract

3D scene reconstruction has important applications to help to produce digital twins of existing buildings. While the community has mostly focused on surface reconstruction or semantic segmentation as separate problems, the joint reconstruction of both volumes and semantics has little been discussed, mostly due to the lack of large scale volume datasets with semantic annotations. In this work, we introduce a new dataset called VASAD for Volume And Semantic Architectural Dataset. It is composed of 6 building models, with full volume description and semantic labels. It approximately represents 62,000 m2 of building floors, making it large enough for the development and evaluation of learning-based approaches. We propose several methods to jointly reconstruct both geometry and semantics and evaluate on the test set of the dataset. We show that the proposed dataset is challenging enough to stimulate research. </a>

BibTeX

@inproceedings{langlois2022vasad,
  title={VASAD: a volume and semantic dataset for building reconstruction from point clouds},
  author={Langlois, Pierre-Alain and Xiao, Yang and Boulch, Alexandre and Marlet, Renaud},
  booktitle={2022 26th International Conference on Pattern Recognition (ICPR)},
  pages={4008--4015},
  year={2022},
  organization={IEEE}
}