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Particularly on small displacements and real-world data, FlowNet cannot compete with variational methods.
#4 best model for Dense Pixel Correspondence Estimation on HPatches
Optical flow estimation has not been among the tasks where CNNs were successful.
To date, top-performing optical flow estimation methods only take pairs of consecutive frames into account.
We investigate two crucial and closely related aspects of CNNs for optical flow estimation: models and training.
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.
#2 best model for Dense Pixel Correspondence Estimation on HPatches
Learning to predict future images from a video sequence involves the construction of an internal representation that models the image evolution accurately, and therefore, to some degree, its content and dynamics.
Our method develops a deep fully convolutional neural network that takes two input frames and estimates pairs of 1D kernels for all pixels simultaneously.
We propose GeoNet, a jointly unsupervised learning framework for monocular depth, optical flow and ego-motion estimation from videos.