What Matters in Unsupervised Optical Flow
Rico Jonschkowski, Austin Stone, Jonathan T. Barron, Ariel Gordon, Kurt Konolige, Anelia Angelova
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."
BibTeX
@inproceedings{eccv2020_whatmattersinuns,
title = {What Matters in Unsupervised Optical Flow},
author = {Rico Jonschkowski and Austin Stone and Jonathan T. Barron and Ariel Gordon and Kurt Konolige and Anelia Angelova},
booktitle = {ECCV 2020},
year = {2020}
}