Occlusion Detection in Dense Stereo Estimation with Convex Optimization

Abstract : In this paper, we propose a dense two-frame stereo algorithm which handles occlusion in a variational framework. Our method is based on a new regularization model which includes both a constraint on the occlusion width and a visibility constraint in nonoccluded areas. The minimization of the resulting energy functional is done by convex relaxation. A post-processing then detects and fills the occluded regions. We also propose a novel dissimilarity measure that combines color and gradient comparison with a variable respective weight, to benefit from the robustness of the comparison based on local variations while avoiding the fattening effect it may generate.
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Dernière modification le : vendredi 10 janvier 2020 - 15:42:10
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Antonin Chambolle, Pascal Monasse, Pauline Tan. Occlusion Detection in Dense Stereo Estimation with Convex Optimization. ICIP'17, IEEE International Conference on Image Processing, Sep 2017, Pekin, China. ⟨10.1109/ICIP.2017.8296741⟩. ⟨hal-01700678⟩

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