Estimation of 3D Indoor Models with Constraint Propagation and Stochastic Reasoning in the Absence of Indoor Measurements

Abstract

This paper presents a novel method for the prediction of building floor plans based on sparse observations in the absence of measurements. We derive the most likely hypothesis using a maximum a posteriori probability approach. Background knowledge consisting of probability density functions of room shape and location parameters is learned from training data. Relations between rooms and room substructures are represented by linear and bilinear constraints. We perform reasoning on different levels providing a problem solution that is optimal with regard to the given information. In a first step, the problem is modeled as a constraint satisfaction problem. Constraint Logic Programming derives a solution which is topologically correct but suboptimal with regard to the geometric parameters. The search space is reduced using architectural constraints and browsed by intelligent search strategies which use domain knowledge. In a second step, graphical models are used for updating the initial hypothesis and refining its continuous parameters. We make use of Gaussian mixtures for model parameters in order to represent background knowledge and to get access to established methods for efficient and exact stochastic reasoning. We demonstrate our approach on different illustrative examples. Initially, we assume that floor plans are rectangular and that rooms are rectangles and discuss more general shapes afterwards. In a similar spirit, we predict door locations providing further important components of 3D indoor models.

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

“…Inspired by the method followed to predict façade models, Loch- Dehbi 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.…”

“…In analogy to regularities such as parallelities, orthogonalities and symmetries characterising building models, the structure of these installations can be modelled as a graph G = (V, E) consisting of vertices V (switches, sockets and intersection points) and edges E (line segments). In order to ensure a valid structure, a constraint satisfaction problem can be solved (Dechter, 2003) similar to the approaches described in Loch- Dehbi and Plümer (2015) and Loch-Dehbi et al (2017) for the prediction of façade and indoor models (cf. Section 3).…”

“…Especially geometric and stochastic reasoning are used in order to model regularities in building models taking the uncertainty of the underlying structures into account. In previous works, we derived successfully 2D (Loch- Dehbi and Plümer, 2011) and 3D (Dehbi et al, 2017a) façade models as well as 3D indoor models from sparse observations (Loch-Dehbi et al, 2017;Dehbi et al, 2017b). We believe that our methods can be transferred for the modelling of latent and not observable structures such as electric installations to enrich 3D building models by BIM relevant structures.…”

Semantic Interpretation of Mobile Laser Scanner Point Clouds in Indoor Scenes Using Trajectories

“…In large-scale there are works by Armeni et al [52] for scene parsing, Mattausch et al [53] using a similarity matrix in cluttered environment and Qi et al [54] using deep learning for object classification. Some other works in the domain of indoor 3D reconstruction from point clouds use semi-automatic approaches to generate BIM models [55][56][57] or stochastic methods to make a hypothesis on generating floor plans [58].Our work is innovative in terms of dealing with glass reflection problems using mobile laser scanners and exploiting the potential of trajectories as a supplementary data produced by MLS systems. This work can be further improved to reconstruct a complete 3D indoor model from complex structures.…”

Globally Consistent Indoor Mapping via a Decoupling Rotation and Translation Algorithm Applied to RGB-D Camera Output

“…However, the collection of 3D model data for the indoor environment is not as easily achieved for those outside, especially for globally-consistent 3D models [3,4]. Indoor 3D model helps GIS (Geographical Information System) to represent, interpret and manage communities and cities [5].…”