PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Combined Depth and Outlier Estimation in Multi-View Stereo
Christoph Strecha, Rik Fransens, Luc Van Gool and Luc Van Gool
In: IEEE Conference on Computer Vision and Pattern Recognition, 17-22 June 2006, New York, USA.


In this paper, we present a generative model based approach to solve the multi-view stereo problem. The input images are considered to be generated by either one of two processes: (i) an inlier process, which generates the pixels which are visible from the reference camera and which obey the constant brightness assumption, and (ii) an outlier process which generates all other pixels. Depth and visibility are jointly modelled as a hiddenMarkov Random Field, and the spatial correlations of both are explicitly accounted for. Inference is made tractable by an EM-algorithm, which alternates between estimation of visibility and depth, and optimisation of model parameters. We describe and compare two implementations of the E-step of the algorithm, which correspond to the Mean Field and Bethe approximations of the free energy. The approach is validated by experiments on challenging real-world scenes, of which two are contaminated by independently moving objects.

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EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
ID Code:2178
Deposited By:Rik Fransens
Deposited On:18 August 2006