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 hidden Markov 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.

Abstract

Abstract. This paper deals with the computation of optical flow and occlusion detection in the case of large displacements. We propose a Bayesian approach to the optical flow problem and solve it by means of differential techniques. The images are regarded as noisy measurements of an underlying 'true' image-function. Additionally, the image data is considered incomplete, in the sense that we do not know which pixels from a particular image are occluded in the other images. We describe an EM-algorithm, which iterates between estimating values for all hidden quantities, and optimizing the current optical flow estimates by differential techniques. The Bayesian way of describing the problem leads to more insight in existing differential approaches, and offers some natural extensions to them. The resulting system involves less parameters and gives an interpretation to the remaining ones. An important new feature is the photometric detection of occluded pixels. We compare the algorithm with existing optical flow methods on ground truth data. The comparison shows that our algorithm generates the most accurate optical flow estimates. We further illustrate the approach with some challenging real-world examples.

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