Predicting building façade structures with multilinear Gaussian graphical models based on few observations

Loch-Dehbi, Sandra; Plümer, Lutz

doi:10.1016/j.compenvurbsys.2015.05.004

Cited by 7 publications

(10 citation statements)

References 31 publications

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“…This intermediate result is adjusted by a statistical component that aligns rooms along corridors where possible and estimates walls completing the floorplan model. Figure 4 gives an Figure 2: Ranked façade hyptheses derived using constraint propagations and stochastic reasoning (Loch-Dehbi and Plümer, 2015). , 26-27 October 2017 This contribution has been peer-reviewed.…”

Section: Generation Of Faç Ade and Indoor Modelsmentioning

confidence: 99%

Section: Generation Of Faç Ade and Indoor Modelsmentioning

confidence: 99%

“…As stated in McLachlan and Peel (2000) each arbitrary probability density function can be approximated by Gaussian mixture models. More details to the applied method are provided by Loch-Dehbi and Plümer (2015).Inspired by the method followed to predict façade models, LochDehbi et al (2017) outlined an approach for the prediction of floorplans and indoor models without the need of indoor measurements. The algorithm profits from an extensive data analysis of shape and location parameters such as width and depth of rooms.…”

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confidence: 99%

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

Dehbi

Haunert

Plümer

2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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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 ...

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Section: Generation Of Faç Ade and Indoor Modelsmentioning

confidence: 99%

Section: Generation Of Faç Ade and Indoor Modelsmentioning

confidence: 99%

mentioning

confidence: 99%

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

Dehbi

Haunert

Plümer

2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Furthermore, the fact that rooms with consequent room numbers are with high probability adjacent is exploited as a soft constraint and helps likewise to limit the combinatorial possibilities. We used the same paradigm for the successful prediction of façade structures based on few observations like the façade width (Loch- Dehbi and Plümer, 2015). The used constraints and their types for the floorplan modelling are listed in Table 1. For floorplan design, Charman (1994) describe a knowledge-based system that generates all possible floorplans satisfying a set of geometric constraints on the rooms (non-overlap, adjacency, minimal/maximal area, minimal/maximal dimension, etc.).…”

Section: Motivation and Contextmentioning

confidence: 99%

Prediction of Building Floorplans Using Logical and Stochastic Reasoning Based on Sparse Observations

Loch-Dehbi

Dehbi

Gröger

et al. 2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:This paper introduces a novel method for the automatic derivation of building floorplans and indoor models. Our approach is based on a logical and stochastic reasoning using sparse observations such as building room areas. No further sensor observations like 3D point clouds are needed. Our method benefits from an extensive prior knowledge of functional dependencies and probability density functions of shape and location parameters of rooms depending on their functional use. The determination of posterior beliefs is performed using Bayesian Networks. Stochastic reasoning is complex since the problem is characterized by a mixture of discrete and continuous parameters that are in turn correlated by non-linear constraints. To cope with this kind of complexity, the proposed reasoner combines statistical methods with constraint propagation. It generates a limited number of hypotheses in a model-based top-down approach. It predicts floorplans based on a-priori localised windows. The use of Gaussian mixture models, constraint solvers and stochastic models helps to cope with the a-priori infinite space of the possible floorplan instantiations. MOTIVATION AND CONTEXTWhile 3D models of the exterior of buildings are meanwhile widely available, 3D building interior models (LoD4 in CityGML, Gröger and Plümer (2012)) are not yet widespread. Tasks such as rescue management, indoor navigation and guide for the blind have led to growing interest in the design and modelling of building interiors. In this context, Turner and Zakhor (2014) proposed an approach for the generation of building floorplans from laser range data based on a triangulation of a 2D sampling of wall positions. Becker et al. (2015) used shape grammars for the reconstruction of 3D indoor models from 3D point clouds. Ochmann et al. (2016) segmented a point cloud into rooms and outside area and reconstruct the scene by solving a labelling problem based on an energy minimization. For the derivation of indoor models, all mentioned approaches rely on dense observations such as 3D point clouds from laserscans or range cameras using mobile mapping systems. This requirement is often not able to be satisfied in an appropriate way so that we have to cope with an a-priori small number of observations instead. As a consequence, our central motivation is to predict unknown substructures in buildings such as floorplans, based only on few observations like the area of rooms and footprints. While footprints are already available by the use of data sources such as official data or Open Street Map, the acquisition of 3D point clouds is far costlier. This paper presents a novel approach for the automatic prediction and generation of building floorplans. Based on sparse observations, we automatically generate a limited number of best hypotheses and provide likelihoods for each solution. Dense observations like 3D point clouds are not required. We follow a modelbased top-down approach and exploit the fact that it is easier to verify or falsify hypotheses than to reconstruct...

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“…The results of the interpreted dataset from the first part are employed to incorporate the observed building part in the model derived or predicted so far. A predicted tree is deduced based on a reasoning process as described in Loch- Dehbi and Plümer (2015). If there is no tree available, a new model is constructed otherwise.…”

Section: Incremental Parsing Of 3d Point Cloudmentioning

confidence: 99%

Incremental Refinement of Façade Models With Attribute Grammar From 3d Point Clouds

Dehbi¹,

Staat²,

Mandtler³

et al. 2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Data acquisition using unmanned aerial vehicles (UAVs) has gotten more and more attention over the last years. Especially in the field of building reconstruction the incremental interpretation of such data is a demanding task. In this context formal grammars play an important role for the top-down identification and reconstruction of building objects. Up to now, the available approaches expect offline data in order to parse an a-priori known grammar. For mapping on demand an on the fly reconstruction based on UAV data is required. An incremental interpretation of the data stream is inevitable. This paper presents an incremental parser of grammar rules for an automatic 3D building reconstruction. The parser enables a model refinement based on new observations with respect to a weighted attribute context-free grammar (WACFG). The falsification or rejection of hypotheses is supported as well. The parser can deal with and adapt available parse trees acquired from previous interpretations or predictions. Parse trees derived so far are updated in an iterative way using transformation rules. A diagnostic step searches for mismatches between current and new nodes. Prior knowledge on façades is incorporated. It is given by probability densities as well as architectural patterns. Since we cannot always assume normal distributions, the derivation of location and shape parameters of building objects is based on a kernel density estimation (KDE). While the level of detail is continuously improved, the geometrical, semantic and topological consistency is ensured. MOTIVATION AND CONTEXT3D building models are nowadays essential for several tasks such as urban and telecommunication planning (Köninger and Bartel, 1998;Knapp and Coors, 2008) and disaster and rescue management (Kolbe et al., 2008) in particular flooding simulations (Schulte and Coors, 2008). Further, the data model CityGML supporting several levels of detail (LoD) is widely used in order to address semantics and exchange city data models. For many applications such as disaster management and visualization in particular in the navigation context, façades (LoD3 models) are important. In order to achieve an interpretation of the observations with good quality models providing prior knowledge are needed. In this context, formal grammars receive increasing attention (Musialski et al., 2012). For the reconstruction of façades Becker (2009) introduced a hybrid approach by the integration of inferred grammar rules into a nonparametric reconstruction process. Ripperda and Brenner (2009) used a probabilistic grammar for the description of façades. The production rules are expanded by the relative frequency of a single rule extracted from a manually collected rule database. Martinović and Van Gool (2013) proposed an approach to learn stochastic attributed grammar rules for two-dimensional façade generation and reconstruction from imagery data. Dehbi et al. (2016) presented a statistical relational based approach for automatic learning of an attribute grammar for bui...

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Predicting building façade structures with multilinear Gaussian graphical models based on few observations

Cited by 7 publications

References 31 publications

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

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

Prediction of Building Floorplans Using Logical and Stochastic Reasoning Based on Sparse Observations

Incremental Refinement of Façade Models With Attribute Grammar From 3d Point Clouds

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