Michal Neoral presents PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume

On 2020-09-09 11:00:00 at G205, Karlovo náměstí 13, Praha 2
Reading group on the work "PWC-Net: CNNs for Optical Flow Using Pyramid,
Warping, and Cost Volume" by Deqing Sun, Xiaodong Yang, Ming-Yu Liu, and Jan
Kautz, CVPR 2018, presented by Michal Neoral.

Paper abstract: 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 cur- rent
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
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

Paper URL:

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analysis of the paper, as well as brainstorming for creative extensions.

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