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Unsupervised learning of optical flow with patch consistency and occlusion estimation
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DOI：10.1016/j.patcog.2019.107191.png)
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Abstract
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Recent works have shown that deep networks can be trained for optical flow estimation without supervision. Based on the photometric constancy assumption, most of these methods adopt the reconstruction loss as the supervision by point-based backward warping. Inspired by the traditional patch matching based approaches, we propose a patch-based consistency to improve the vanilla unsupervised learning method Ren et al. [1]. Instead of only comparing the corresponding pixel intensity, we locate the correspondence by using the image patches with census transform, which is more robust for the illumination variation and occlusion. Moreover, a novel parallel branch is devised to estimate a soft occlusion mask jointly in an unsupervised way. The mask is adopted to weight our patch-based consistency loss to alleviate the influence of the occlusion. The plenty of experiments have been implemented on Flying Chairs, KITTI and MPI-Sintel benchmarks. The results show that our method is efficient and outperforms the peer unsupervised learning methods that are using the FlowNet-liked network. (C) 2019 Published by Elsevier Ltd.
Keywords:
Patch consistency
Optical flow estimation
Occlusion estimation
Unsupervised learning
Deep learning
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