Data driven estimation of building interior plans

Rosser, Julian F.; Smith, Gavin; Morley, Jeremy

Abstract

This work investigates constructing plans of building interiors using learned building measurements. In particular, we address the problem of accurately estimating dimensions of rooms when measurements of the interior space have not been captured. Our approach focuses on learning the geometry, orientation and occurrence of rooms from a corpus of real-world building plan data to form a predictive model. The trained predictive model may then be queried to generate estimates of room dimensions and orientations. These estimates are then integrated with the overall building footprint and iteratively improved using a two-stage optimisation process to form complete interior plans.
The approach is presented as a semi-automatic method for constructing plans which can cope with a limited set of known information and constructs likely representations of building plans through modelling of soft and hard constraints. We evaluate the method in the context of estimating residential house plans and demonstrate that predictions can effectively be used for constructing plans given limited prior knowledge about the types of rooms and their topology.

Citation

Rosser, J. F., Smith, G., & Morley, J. (in press). Data driven estimation of building interior plans. International Journal of Geographical Information Science, 31(8), https://doi.org/10.1080/13658816.2017.1313980

• Journal Article Type: Article
• Acceptance Date: Mar 27, 2017
• Online Publication Date: Apr 13, 2017
• Deposit Date: Mar 31, 2017
• Publicly Available Date: Apr 13, 2017
• Journal: International Journal of Geographical Information Science
• Print ISSN: 1365-8816
• Electronic ISSN: 1365-8824
• Publisher: Taylor and Francis
• Peer Reviewed: Peer Reviewed
• Volume: 31
• Issue: 8
• DOI: https://doi.org/10.1080/13658816.2017.1313980
• Keywords: Building modelling; optimisation; indoor mapping; prediction
• Public URL: https://nottingham-repository.worktribe.com/output/855985
• Publisher URL: http://www.tandfonline.com/doi/full/10.1080/13658816.2017.1313980
• Contract Date: Mar 31, 2017