|TREND||DATASET||BEST METHOD||PAPER TITLE||PAPER||CODE||COMPARE|
Particularly on small displacements and real-world data, FlowNet cannot compete with variational methods.
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.
Ranked #2 on Dense Pixel Correspondence Estimation on HPatches
We learn to compute optical flow by combining a classical spatial-pyramid formulation with deep learning.
Ranked #7 on Dense Pixel Correspondence Estimation on HPatches
We propose a novel framework for finding correspondences in images based on a deep neural network that, given two images and a query point in one of them, finds its correspondence in the other.
Ranked #1 on Dense Pixel Correspondence Estimation on KITTI 2012
We introduce a novel matching algorithm, called DeepMatching, to compute dense correspondences between images.
Ranked #3 on Dense Pixel Correspondence Estimation on HPatches