The DOI matches its Crossref record; 65 of the 65 references it cites were found in a registry or an index.
PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume
Open accessAbstract
We present a compact but effective CNN model for optical flow, called PWC-Net. PWC-Net has been designed according to simple and well-established principles: pyramidal processing, warping, and the use of a cost volume. Cast in a learnable feature pyramid, PWC-Net uses the current optical flow estimate to warp the CNN features of the second image. It then uses the warped features and features of the first image to construct a cost volume, which is processed by a CNN to estimate the optical flow. PWC-Net is 17 times smaller in size and easier to train than the recent FlowNet2 model. Moreover, it outperforms all published optical flow methods on the MPI Sintel final pass and KITTI 2015 benchmarks, running at about 35 fps on Sintel resolution (1024 Ã × 436) images. Our models are available on our project website.
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Cited by
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Digging Into Self-Supervised Monocular Depth Estimation
Godard, C., Aodha, O. M., Firman, M., & Brostow, G. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00393 · Cited by 2,561
Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-Shot Cross-Dataset Transfer
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Detection and tracking meet drones challenge
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Edvr: video restoration with enhanced deformable convolutional networks
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Metadata
- Authors
- Sun, D., Yang, X., Liu, M.-Y., & Kautz, J.
- Venue
- 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2018-06
- Pages
- 8934-8943
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2963782415
- Cited by
- 2,648
- References
- 65
- FWCI
- 81.23
- Type
- conference paper
- Language
- en
- Open access
- green
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