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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 32

UnFlow: Unsupervised Learning of Optical Flow With a Bidirectional Census Loss

March 15, 2023

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Published Date: 2018-02-08

Registration: ISSN 2374-3468 (Online) ISSN 2159-5399 (Print)

Copyright: Published by AAAI Press, Palo Alto, California USA Copyright © 2018, Association for the Advancement of Artificial Intelligence All Rights Reserved.

Authors

Simon Meister

TU Darmstadt


Junhwa Hur

TU Darmstadt


Stefan Roth

TU Darmstadt


DOI:

10.1609/aaai.v32i1.12276


Abstract:

In the era of end-to-end deep learning, many advances in computer vision are driven by large amounts of labeled data. In the optical flow setting, however, obtaining dense per-pixel ground truth for real scenes is difficult and thus such data is rare. Therefore, recent end-to-end convolutional networks for optical flow rely on synthetic datasets for supervision, but the domain mismatch between training and test scenarios continues to be a challenge. Inspired by classical energy-based optical flow methods, we design an unsupervised loss based on occlusion-aware bidirectional flow estimation and the robust census transform to circumvent the need for ground truth flow. On the KITTI benchmarks, our unsupervised approach outperforms previous unsupervised deep networks by a large margin, and is even more accurate than similar supervised methods trained on synthetic datasets alone. By optionally fine-tuning on the KITTI training data, our method achieves competitive optical flow accuracy on the KITTI 2012 and 2015 benchmarks, thus in addition enabling generic pre-training of supervised networks for datasets with limited amounts of ground truth.

Topics: AAAI

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HOW TO CITE:

Simon Meister||Junhwa Hur||Stefan Roth UnFlow: Unsupervised Learning of Optical Flow With a Bidirectional Census Loss Proceedings of the AAAI Conference on Artificial Intelligence, 32 (2018) .

Simon Meister||Junhwa Hur||Stefan Roth UnFlow: Unsupervised Learning of Optical Flow With a Bidirectional Census Loss AAAI 2018, .

Simon Meister||Junhwa Hur||Stefan Roth (2018). UnFlow: Unsupervised Learning of Optical Flow With a Bidirectional Census Loss. Proceedings of the AAAI Conference on Artificial Intelligence, 32, .

Simon Meister||Junhwa Hur||Stefan Roth. UnFlow: Unsupervised Learning of Optical Flow With a Bidirectional Census Loss. Proceedings of the AAAI Conference on Artificial Intelligence, 32 2018 p..

Simon Meister||Junhwa Hur||Stefan Roth. 2018. UnFlow: Unsupervised Learning of Optical Flow With a Bidirectional Census Loss. "Proceedings of the AAAI Conference on Artificial Intelligence, 32". .

Simon Meister||Junhwa Hur||Stefan Roth. (2018) "UnFlow: Unsupervised Learning of Optical Flow With a Bidirectional Census Loss", Proceedings of the AAAI Conference on Artificial Intelligence, 32, p.

Simon Meister||Junhwa Hur||Stefan Roth, "UnFlow: Unsupervised Learning of Optical Flow With a Bidirectional Census Loss", AAAI, p., 2018.

Simon Meister||Junhwa Hur||Stefan Roth. "UnFlow: Unsupervised Learning of Optical Flow With a Bidirectional Census Loss". Proceedings of the AAAI Conference on Artificial Intelligence, 32, 2018, p..

Simon Meister||Junhwa Hur||Stefan Roth. "UnFlow: Unsupervised Learning of Optical Flow With a Bidirectional Census Loss". Proceedings of the AAAI Conference on Artificial Intelligence, 32, (2018): .

Simon Meister||Junhwa Hur||Stefan Roth. UnFlow: Unsupervised Learning of Optical Flow With a Bidirectional Census Loss. AAAI[Internet]. 2018[cited 2023]; .


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
Artificial Intelligence 1900 Embarcadero Road, Suite
101, Palo Alto, California 94303 All Rights Reserved

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