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Title: Modelling and interpretation of architecture from several images
Author: Dick, A.
Awarding Body: University of Cambridge
Current Institution: University of Cambridge
Date of Award: 2002
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This thesis focuses on the automatic acquisition of 3-dimensional (3D) architectural models from short image sequences. An architectural model is defined as a set of planes corresponding to walls which contain a variety of parameterised primitives such as doors and windows. As well as parameters defining its shape and texture, each primitive has a label that identifies it as a particular architectural component. Assigning a label to each primitive means that model estimation involves interpreting the scene as well as recovering its shape and texture. This enables reasoning about the scene, which makes estimation of the visible parts of the model more reliable, and means that structure and texture can be inferred in areas of the model which are unseen in any image. Having semantic knowledge of the scene also enables model enhancement, as all windows in a scene, for example, can be given a standard window texture, or made shiny and semi-transparent for increased realism. The representation of a model as a set of simple, compact primitives allows it to be estimated accurately from a small number of images, and manipulated and rendered in a straightforward manner. In this thesis a Bayesian probabilistic framework is developed in which model acquisition is formulated as a search for maximum a posteriori (MAP) model parameters. A prior distribution is defined for the parameters of the model, and its validity is tested by simulating draws from it and verifying that it does indeed generate plausible buildings under varying conditions. Two likelihood functions are also defined and tested. A practical algorithm is then developed to find MAP model parameters based on these likelihood and prior distributions. The algorithm is tested on a variety of architectural image sequences.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available