Spatially consistent 3D motion segmentation

3D motion segmentation is the task to cluster corresponding points in multiple (at least two) images, so that each cluster corresponds to a 3D motion in the underlying 3D scene. The problem can be divided into two stages: first, all motion models required to describe the scene have to be found. Second, each correspondence has to be assigned to the correct model. This paper is concerned with the second part. A natural procedure is to assign each correspondence to a motion, such that the a-posteriori likelihood of the description is maximized. However, this is not trivial, since the likelihoods of different correspondences are not independent: neighboring correspondences tend to belong to the same motion, a fact commonly referred to as "smoothness" or "spatial consistency". To account for this fact, we model the set of multiview correspondences as an irregular Markov random field (MRF). The MRF is then optimized with recent graph-based methods, and individual clique potentials are inspected for fine-grained outlier detection.