PREDICTION OF BUILDING FLOORPLANS USING LOGICAL AND STOCHASTIC REASONING BASED ON SPARSE OBSERVATIONS
| Autor: | Lutz Plümer, Gerhard Gröger, Y. Dehbi, Sandra Loch-Dehbi |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
lcsh:Applied optics. Photonics
Mathematical optimization 010504 meteorology & atmospheric sciences Stochastic modelling lcsh:T 0211 other engineering and technologies Bayesian network lcsh:TA1501-1820 02 engineering and technology Semantic reasoner Mixture model 01 natural sciences lcsh:Technology Constraint (information theory) Logical conjunction lcsh:TA1-2040 Constraint logic programming Local consistency lcsh:Engineering (General). Civil engineering (General) Algorithm 021101 geological & geomatics engineering 0105 earth and related environmental sciences Mathematics |
| Zdroj: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol IV-2/W1, Pp 265-270 (2016) |
| ISSN: | 2194-9050 2194-9042 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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