Flownet: Learning optical flow with convolutional networks
Abstract
Convolutional neural networks (CNNs) have recently been very successful in a variety of computer vision tasks, especially on those linked to recognition. Optical flow estimation has not been among the tasks CNNs succeeded at. In this paper we construct CNNs which are capable of solving the optical flow estimation problem as a supervised learning task. We propose and compare two architectures: a generic architecture and another one including a layer that correlates feature vectors at different image locations. Since existing ground truth data sets are not sufficiently large to train a CNN, we generate a large synthetic Flying Chairs dataset. We show that networks trained on this unrealistic data still generalize very well to existing datasets such as Sintel and KITTI, achieving competitive accuracy at frame rates of 5 to 10 fps.
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The Scene Flow model is trained in highly dynamic synthetic environments and is able to estimate large flow magnitude caused by moving objects.
“On the other hand, Scene Flow model is trained in highly dynamic synthetic environments, i.e. able to estimate large flow magnitude caused by moving objects.”
From DF-VO: What Should Be Learnt for Visual Odometry? (2021) · cites this paper as Dosovitskiy et al. 2015
LiteFlowNet trained on the synthetic Scene Flow dataset can generalize well in real-world scenarios, though sometimes artifacts are present in some regions.
“LiteFlowNet shows good generalization ability. LiteFlowNet trained on a synthetic dataset (Scene Flow(Dosovitskiy et al. 2015)) can generalize well in realworld scenarios, though sometimes artifacts present in some regions.”
From DF-VO: What Should Be Learnt for Visual Odometry? (2021) · cites this paper as Dosovitskiy et al. 2015
Uzunova et al. designed a network for registration between 2D brain MRI and 2D cardiac MRI, with ground truth generated using statistical appearance models (SAMs), adapted FlowNet architecture, and obtained outperforming results.
“Uzunova et al. (78) designed a network for the registration between 2D brain MRI and 2D cardiac MRI. Their ground truth is generated utilizing statistical appearance models (SAMs). They adapted FlowNet (79) architecture and obtained outperforming results.”
From A review of deep learning-based deformable medical image registration (2022) · cites this paper as [79]
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- Authors
- Dosovitskiy, A., Fischer, P., Ilg, E., Hausser, P., Hazirbas, C., Golkov, V., Smagt, P. V. D., Cremers, D., & Brox, T.
- Affiliations
- University of Freiburg (DE)
- Technical University of Munich (DE)
- Venue
- 2015 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2015-12
- Pages
- 2758-2766
- OpenAlex
- W764651262
- Cited by
- 4,313
- References
- 44
- Citations by year
- 2026: 182 · 2025: 342 · 2024: 462 · 2023: 550 · 2022: 559 · 2021: 647 · 2020: 579 · 2019: 469 · 2018: 278 · 2017: 184 · 2016: 57 · 2015: 3
- FWCI
- 71.36
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- Funders
- Deutsche Telekom Stiftung
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Keywords
- optical flow, convolutional neural networks, supervised learning, synthetic training data, KITTI, video frame rate
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