This paper presents a novel variational approach for the joint estimation of scene flow and occlusions. Our
method does not assume that a depth sensor is available. Instead, we use a stereo sequence and exploit the
fact that points that are occluded in time, might be visible from the other view and thus the 3D geometry can
be densely reinforced in an appropriate manner through a simultaneous motion occlusion characterization.
Moreover, large displacements are correctly captured thanks to an optimization ...
This paper presents a novel variational approach for the joint estimation of scene flow and occlusions. Our
method does not assume that a depth sensor is available. Instead, we use a stereo sequence and exploit the
fact that points that are occluded in time, might be visible from the other view and thus the 3D geometry can
be densely reinforced in an appropriate manner through a simultaneous motion occlusion characterization.
Moreover, large displacements are correctly captured thanks to an optimization strategy that uses a set of
sparse image correspondences to guide the minimization process. We include qualitative and quantitative
experimental results on several datasets illustrating that both proposals help to improve the baseline results.
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