HouseCraft: Building Houses from Rental Ads and Street Views
Abstract
In this paper, we utilize rental ads to create realistic textured 3D models of building exteriors. In particular, we exploit the address of the property and its floorplan, which are typically available in the ad. The address allows us to extract Google StreetView images around the building, while the building’s floorplan allows for an efficient parametrization of the building in 3D via a small set of random variables. We propose an energy minimization framework which jointly reasons about the height of each floor, the vertical positions of windows and doors, as well as the precise location of the building in the world’s map, by exploiting several geometric and semantic cues from the StreetView imagery. To demonstrate the effectiveness of our approach, we collected a new dataset with 174 houses by crawling a popular rental website. Our experiments show that our approach is able to precisely estimate the geometry and location of the property, and can create realistic 3D building models.
Cite
Text
Chu et al. "HouseCraft: Building Houses from Rental Ads and Street Views." European Conference on Computer Vision, 2016. doi:10.1007/978-3-319-46466-4_30Markdown
[Chu et al. "HouseCraft: Building Houses from Rental Ads and Street Views." European Conference on Computer Vision, 2016.](https://mlanthology.org/eccv/2016/chu2016eccv-housecraft/) doi:10.1007/978-3-319-46466-4_30BibTeX
@inproceedings{chu2016eccv-housecraft,
title = {{HouseCraft: Building Houses from Rental Ads and Street Views}},
author = {Chu, Hang and Wang, Shenlong and Urtasun, Raquel and Fidler, Sanja},
booktitle = {European Conference on Computer Vision},
year = {2016},
pages = {500-516},
doi = {10.1007/978-3-319-46466-4_30},
url = {https://mlanthology.org/eccv/2016/chu2016eccv-housecraft/}
}