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What Matters in Unsupervised Optical Flow | | Rico Jonschkowski
; Austin Stone
; Jonathan T. Barron
; Ariel Gordon
; Kurt Konolige
; Anelia Angelova
; | | Date: |
8 Jun 2020 | | Abstract: | We systematically compare and analyze a set of key components in unsupervised
optical flow to identify which photometric loss, occlusion handling, and
smoothness regularization is most effective. Alongside this investigation we
construct a number of novel improvements to unsupervised flow models, such as
cost volume normalization, stopping the gradient at the occlusion mask,
encouraging smoothness before upsampling the flow field, and continual
self-supervision with image resizing. By combining the results of our
investigation with our improved model components, we are able to present a new
unsupervised flow technique that significantly outperforms the previous
unsupervised state-of-the-art and performs on par with supervised FlowNet2 on
the KITTI 2015 dataset, while also being significantly simpler than related
approaches. | | Source: | arXiv, 2006.4902 | | Services: | Forum | Review | PDF | Favorites | |
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