Grammar-Supported 3d Indoor Reconstruction From Point Clouds for “As-Built” Bim

Abstract

ABSTRACT:The paper presents a grammar-based approach for the robust automatic reconstruction of 3D interiors from raw point clouds. The core of the approach is a 3D indoor grammar which is an extension of our previously published grammar concept for the modeling of 2D floor plans. The grammar allows for the modeling of buildings whose horizontal, continuous floors are traversed by hallways providing access to the rooms as it is the case for most office buildings or public buildings like schools, hospitals or hotels. The grammar is designed in such way that it can be embedded in an iterative automatic learning process providing a seamless transition from LOD3 to LOD4 building models. Starting from an initial low-level grammar, automatically derived from the window representations of an available LOD3 building model, hypotheses about indoor geometries can be generated. The hypothesized indoor geometries are checked against observation data -here 3D point clouds -collected in the interior of the building. The verified and accepted geometries form the basis for an automatic update of the initial grammar. By this, the knowledge content of the initial grammar is enriched, leading to a grammar with increased quality. This higher-level grammar can then be applied to predict realistic geometries to building parts where only sparse observation data are available. Thus, our approach allows for the robust generation of complete 3D indoor models whose quality can be improved continuously as soon as new observation data are fed into the grammar-based reconstruction process. The feasibility of our approach is demonstrated based on a real-world example.

Exploiting Indoor Mobile Laser Scanner Trajectories for Semantic Interpretation of Point Clouds

“…IMLS systems, in addition to the point clouds, provide a continuous trajectory of device locations instead of few discrete station points in TLS. Current methods for indoor reconstruction and semantic labelling use mainly TLSs (Becker et al, 2015;Mura et al, 2014a;Oesau et al, 2014) or RGB-Depth data (Armeni et al, 2016;Khan et al, 2015). If MLS data is used as in (Xiao and Furukawa, 2014), the benefit of trajectory data is not exploited.…”

Current State of the Art Historic Building Information Modelling

“…Procedural modelling has traditionally been used in applications such as film and gaming where content can be randomly generated based on rules and algorithms. The use of shape grammars in procedural modelling has gained a lot of interest and is now being used to generate content for architectural modelling (Muller et al, 2006, Dylla et al, 2010, Hohmann et al, 2009, Becker and Haala, 2009, Becker et al, 2015. Shape grammars originally introduced by Stiny and Gips (1972) are derived from formal grammars and consist of a set of basic vocabulary shapes (terminals and non-terminals) and a set of production rules to transform these shapes to create 3D content.…”

The Isprs Benchmark on Indoor Modelling

“…In practice, most existing methods reconstruct geometric elements only (Becker et al, 2015;Díaz-Vilariño et al, 2015;Mura et al, 2016;Oesau et al, 2014;Sanchez and Zakhor, 2012;Thomson and Boehm, 2015;Valero et al, 2012;Xiao and Furukawa, 2012;Xiong et al, 2013), while a few can model spaces and the topological relations between them (Khoshelham and Díaz-Vilariño, 2014;Tran et al, 2017). Nonetheless, a comprehensive evaluation should take into account all three components of an indoor model.…”