Data driven estimation of building interior plans
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, orien...
| Main Authors: | , , |
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| Format: | Article |
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Taylor & Francis
2017
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| Online Access: | https://eprints.nottingham.ac.uk/41668/ |
| _version_ | 1848796327203307520 |
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| author | Rosser, Julian F. Smith, Gavin Morley, Jeremy |
| author_facet | Rosser, Julian F. Smith, Gavin Morley, Jeremy |
| author_sort | Rosser, Julian F. |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T19:46:13Z |
| format | Article |
| id | nottingham-41668 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:46:13Z |
| publishDate | 2017 |
| publisher | Taylor & Francis |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-416682020-05-04T18:41:51Z https://eprints.nottingham.ac.uk/41668/ Data driven estimation of building interior plans Rosser, Julian F. Smith, Gavin Morley, Jeremy 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. Taylor & Francis 2017-04-13 Article PeerReviewed 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 Building modelling; optimisation; indoor mapping; prediction http://www.tandfonline.com/doi/full/10.1080/13658816.2017.1313980 doi:10.1080/13658816.2017.1313980 doi:10.1080/13658816.2017.1313980 |
| spellingShingle | Building modelling; optimisation; indoor mapping; prediction Rosser, Julian F. Smith, Gavin Morley, Jeremy Data driven estimation of building interior plans |
| title | Data driven estimation of building interior plans |
| title_full | Data driven estimation of building interior plans |
| title_fullStr | Data driven estimation of building interior plans |
| title_full_unstemmed | Data driven estimation of building interior plans |
| title_short | Data driven estimation of building interior plans |
| title_sort | data driven estimation of building interior plans |
| topic | Building modelling; optimisation; indoor mapping; prediction |
| url | https://eprints.nottingham.ac.uk/41668/ https://eprints.nottingham.ac.uk/41668/ https://eprints.nottingham.ac.uk/41668/ |