Joint large displacement scene flow and occlusion variational estimation
Joint large displacement scene flow and occlusion variational estimation
Citació
- Palomares RP, Haro G, Ballester C. Joint large displacement scene flow and occlusion variational estimation. In: Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017); 2017 Feb 27 - Mar 1; Porto, Portugal. Setúbal: Scitepress; 2017. p. 172-80. DOI: 10.5220/0006110601720180
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Descripció
Resum
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.Descripció
Comunicació presentada al congrés International Conference on Computer Vision Theory and Applications (VISSAP) celebrat a Porto (Portugal) del 27 de febrer a l'1 de març de 2017.