Housecraft: Building Houses from Rental Ads and Street Views

Hang Chu, Shenlong Wang, Raquel Urtasun, Sanja Fidler

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish (US)
Title of host publicationComputer Vision - 14th European Conference, ECCV 2016, Proceedings
EditorsBastian Leibe, Jiri Matas, Nicu Sebe, Max Welling
Number of pages17
ISBN (Print)9783319464657
StatePublished - 2016
Externally publishedYes
Event14th European Conference on Computer Vision, ECCV 2016 - Amsterdam, Netherlands
Duration: Oct 11 2016Oct 14 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9910 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other14th European Conference on Computer Vision, ECCV 2016


  • 3D reconstruction
  • 3D scene understanding
  • Localization

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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