Data-driven estimation of building interior plans

被引:9
|
作者
Rosser, Julian F. [1 ]
Smith, Gavin [1 ]
Morley, Jeremy G. [2 ]
机构
[1] Univ Nottingham, Nottingham, England
[2] Ordnance Survey, Explorer House,Adanac Dr, Southampton, Hants, England
基金
英国工程与自然科学研究理事会;
关键词
Building modelling; optimisation; indoor mapping; prediction; DESIGN; SENSOR; LAYOUT;
D O I
10.1080/13658816.2017.1313980
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:1652 / 1674
页数:23
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