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Rosser, Julian F., Smith, Gavin and Morley, Jeremy (2017) Data driven estimation of building interior plans. International Journal of Geographical Information Science, 31 (8). pp. 1652-1674. ISSN 1365-8824

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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.

Item Type:	Article
RIS ID:	https://nottingham-repository.worktribe.com/output/855985
Keywords:	Building modelling; optimisation; indoor mapping; prediction
Schools/Departments:	University of Nottingham, UK > Faculty of Engineering
Identification Number:	https://doi.org/10.1080/13658816.2017.1313980
Depositing User:	Eprints, Support
Date Deposited:	31 Mar 2017 10:34
Last Modified:	04 May 2020 18:41
URI:	https://eprints.nottingham.ac.uk/id/eprint/41668

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