Résumé: This paper describes the automatic acquisition of three dimensional architectural models from short image sequences. The approach is Bayesian and model based. Bayesian methods necessitate the formulation of a prior distribution; however designing a generative model for buildings is a difficult task. In order to overcome this a building is described as a set of walls together with a 'Lego' kit of parameterised primitives, such as doors or windows. A prior on wall layout and a prior on the parameters of each primitive can then be defined. Part of this prior is learnt from training data and part comes from expert architects. The validity of the prior is tested by generating example buildings using MCMC and verifying that plausible buildings are generated under varying conditions. The same MCMC machinery can also be used for optimising the structure recovery, this time generating a range of possible solutions from the posterior. The fact that a range of solutions can be presented allows the user to select the best when the structure recovery is ambiguous.

Langue: Anglais