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Found 4 papers, 4 papers with code
What Makes Good Synthetic Training Data for Learning Disparity and Optical Flow Estimation?
The finding that very large networks can be trained efficiently and reliably has led to a paradigm shift in computer vision from engineered solutions to learning formulations.
DeMoN: Depth and Motion Network for Learning Monocular Stereo
In this paper we formulate structure from motion as a learning problem.
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
Particularly on small displacements and real-world data, FlowNet cannot compete with variational methods.
Dense Pixel Correspondence Estimation
Optical Flow Estimation
+1
A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation
By combining a flow and disparity estimation network and training it jointly, we demonstrate the first scene flow estimation with a convolutional network.
