Joint Large Displacement Scene Flow and Occlusion Variational Estimation

Roberto P. Palomares, Gloria Haro, Coloma Ballester

2017

Abstract

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.

Paper Citation

in Harvard Style

P. Palomares R., Haro G. and Ballester C. (2017). 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 - Volume 6: VISAPP, (VISIGRAPP 2017) ISBN 978-989-758-227-1, pages 172-180. DOI: 10.5220/0006110601720180

in Bibtex Style

@conference{visapp17,
author={Roberto P. Palomares and Gloria Haro and Coloma Ballester},
title={Joint Large Displacement Scene Flow and Occlusion Variational Estimation},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 6: VISAPP, (VISIGRAPP 2017)},
year={2017},
pages={172-180},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006110601720180},
isbn={978-989-758-227-1},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 6: VISAPP, (VISIGRAPP 2017)
TI - Joint Large Displacement Scene Flow and Occlusion Variational Estimation
SN - 978-989-758-227-1
AU - P. Palomares R.
AU - Haro G.
AU - Ballester C.
PY - 2017
SP - 172
EP - 180
DO - 10.5220/0006110601720180