The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
UnFlow: Unsupervised Learning of Optical Flow With a Bidirectional Census Loss
Open accessAbstract
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.
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Cites
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Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Are we ready for autonomous driving? The KITTI vision benchmark suite
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The Cityscapes Dataset for Semantic Urban Scene Understanding
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Self-citationVision meets robotics: The KITTI dataset
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Flownet: Learning optical flow with convolutional networks
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Object scene flow for autonomous vehicles
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A Database and Evaluation Methodology for Optical Flow
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“Similar observations hold, for example, on the Middlebury dataset (Baker et al. 2011).”
ResultSelf-citationA Naturalistic Open Source Movie for Optical Flow Evaluation
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The Cityscapes Dataset for Semantic Urban Scene Understanding
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Cited by
Self-Supervised Visual Feature Learning With Deep Neural Networks: A Survey
Jing, L., & Tian, Y. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.2992393 · Cited by 2,036
GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose
Yin, Z., & Shi, J. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00212 · Cited by 1,256
Ranjan, A., Jampani, V., Balles, L., Kim, K., Sun, D., Wulff, J., & Black, M. J. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.01252 · Cited by 632
EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras
Zhu, A., Yuan, L., Chaney, K., & Daniilidis, K. (2018) · Robotics: Science and Systems XIV · doi:10.15607/rss.2018.xiv.062 · Cited by 470
Learning Correspondence From the Cycle-Consistency of Time
Wang, X., Jabri, A., & Efros, A. A. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00267 · Cited by 445
Metadata
- Authors
- Meister, S., Hur, J., & Roth, S.
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2018-04-27
- Volume
- 32
- Issue
- 1
- ISSN
- 2374-3468
- 2159-5399
- OpenAlex
- W2963891416
- Cited by
- 520
- References
- 11
- FWCI
- 32.15
- Type
- journal article
- Language
- en
- Open access
- green
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- Meister, S., Hur, J., & Roth, S. (2018). UnFlow: Unsupervised Learning of Optical Flow With a Bidirectional Census Loss. Proceedings of the AAAI Conference on Artificial Intelligence. https://doi.org/10.1609/aaai.v32i1.12276
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