Stochastic and Geometric Reasoning for Indoor Building Models With Electric Installations – Bridging the Gap Between Gis and Bim

Abstract

ABSTRACT:3D city and building models according to CityGML encode the geometry, represent the structure and model semantically relevant building parts such as doors, windows and balconies. Building information models support the building design, construction and the facility management. In contrast to CityGML, they include also objects which cannot be observed from the outside. The three dimensional indoor models characterize a missing link between both worlds. Their derivation, however, is expensive. The semantic automatic interpretation of 3D point clouds of indoor environments is a methodically demanding task. The data acquisition is costly and difficult. The laser scanners and image-based methods require the access to every room. Based on an approach which does not require an additional geometry acquisition of building indoors, we propose an attempt for filling the gaps between 3D building models and building information models. Based on sparse observations such as the building footprint and room areas, 3D indoor models are generated using combinatorial and stochastic reasoning. The derived models are expanded by a-priori not observable structures such as electric installation. Gaussian mixtures, linear and bi-linear constraints are used to represent the background knowledge and structural regularities. The derivation of hypothesised models is performed by stochastic reasoning using graphical models, Gauss-Markov models and MAP-estimators. MOTIVATION AND CONTEXTBuilding information models (BIMs) are widely used for building design, preconstruction analysis and construction planing. However, such models for as-built state are needed for a wide range of buildings. They are important and have widespread benefits for many tasks such as facility management. While BIMs for new constructions are manually designed based on various software, BIMs for existing buildings have to be derived from observations like 3D point clouds from laserscanners or range cameras. For the derivation of such models, especially for indoor environments, the necessary measurements are both cost and time extensive. In comparison to outdoor models where mainly airborne or terrestrial platforms are used for capturing data, the derivation of observations for indoor models is rather different. Every single room must be entered and scanned. Besides, the modelling of wall elements, doors, windows and ceilings turns out to be a difficult task, since they are often concealed by different kinds of furniture. Hence, it is necessary to identify and eliminate them from the model. A BIM contains both semantically and geometrically rich information for the representation of the physical and functional features of a facility, however, surrounding information is not included (Rafiee et al., 2014). In contrast to BIM, a Geographic Information System (GIS) enables to perform spatial analysis incorporating the outdoor environment's functional and physical spatial relationships. Nevertheless, the contained building information is not rich enough for tasks such ...

Suitability Assessment of Different Sensors to Detect Hidden Installations for as-Built Bim

“…The interested reader is also referred to the study conducted by Pintore et al (2020) which provides an overview on input sources, the structure of output models and a possible reconstruction pipeline as well as future research trends. Dehbi et al (2017) suggested a stochastic approach to obtain a model from sparse observations rather than relying on conducting intensive surveying overhead.…”