Occlusions, Motion and Depth Boundaries with a Generic Network for Disparity, Optical Flow or Scene Flow Estimation
Abstract
Occlusions play an important role in optical flow and disparity estimation, since matching costs are not available in occluded areas and occlusions indicate motion boundaries. Moreover, occlusions are relevant for motion segmentation and scene flow estimation. In this paper, we present an efficient learning-based approach to estimate occlusion areas jointly with optical flow or disparities. The estimated occlusions and motion boundaries clearly improve over the state of the art. Moreover, we present networks with state-of-the-art performance on the popular KITTI benchmark and good generic performance. Making use of the estimated occlusions, we also show imprved results on motion segmentation and scene flow estimation.
Cite
Text
Ilg et al. "Occlusions, Motion and Depth Boundaries with a Generic Network for Disparity, Optical Flow or Scene Flow Estimation." Proceedings of the European Conference on Computer Vision (ECCV), 2018. doi:10.1007/978-3-030-01258-8_38Markdown
[Ilg et al. "Occlusions, Motion and Depth Boundaries with a Generic Network for Disparity, Optical Flow or Scene Flow Estimation." Proceedings of the European Conference on Computer Vision (ECCV), 2018.](https://mlanthology.org/eccv/2018/ilg2018eccv-occlusions/) doi:10.1007/978-3-030-01258-8_38BibTeX
@inproceedings{ilg2018eccv-occlusions,
title = {{Occlusions, Motion and Depth Boundaries with a Generic Network for Disparity, Optical Flow or Scene Flow Estimation}},
author = {Ilg, Eddy and Saikia, Tonmoy and Keuper, Margret and Brox, Thomas},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
year = {2018},
doi = {10.1007/978-3-030-01258-8_38},
url = {https://mlanthology.org/eccv/2018/ilg2018eccv-occlusions/}
}