Abstract
ABSTRACTThis 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.
Highlights
Measurement and modelling of interior spaces has received attention in a variety disciplines
Evaluation of the classification accuracy was completed for area, aspect and orientation predictions
This paper presented a modelling approach for constructing building plans using learned room dimensions, topology and orientations in combination with an optimisation model to generate metric scale building plans
Summary
Measurement and modelling of interior spaces has received attention in a variety disciplines. Approaches to tackle this issue often use laser scanners for making high-resolution interior models. Mobile and human-mounted systems (such as Bosse et al 2012) reduce the difficulty and time-consuming nature of this survey (Thomson et al 2013). These systems are not available to non-professional users or those requiring a low-cost measurement solution. Low-cost systems using active-sensing technology have demonstrated reasonable accuracy, but these approaches still require the addition of hardware such as Kinect (Henry et al 2012), projector (Kim et al 2012) or laser range finders (Nguyen et al 2013)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Geographical Information Science
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
