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

Understand

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.