Original paper
SDOF-GAN: Symmetric Dense Optical Flow Estimation With Generative Adversarial Networks
Abstract
There is a growing consensus in computer vision that symmetric optical flow estimation constitutes a better model than a generic asymmetric one for its independence of the selection of source/target image. Yet, convolutional neural networks (CNNs), that are considered the de facto standard vision model, deal with the asymmetric case only in most cutting-edge CNNs-based optical flow techniques. We bridge this gap by introducing a novel model...
Paper Details
Title
SDOF-GAN: Symmetric Dense Optical Flow Estimation With Generative Adversarial Networks
Published Date
Jan 1, 2021
Volume
30
Pages
6036 - 6049
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