lmb-freiburg / flownet2

FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks

Home Page:https://lmb.informatik.uni-freiburg.de/Publications/2017/IMKDB17/

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Train dataset

xubin1994 opened this issue · comments

Hi,I saw on the sceneflow dataset website that you mentioned that you have customized a Flything3D subset. In this subset you ommitted some extremely hard samples. So my question is what is the standards for the "extremely hard samples"?And If using the original Flything 3D subset, what impact will it have on the network?
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IIRC "extremely hard" samples have disparity magnitudes > 300 pixels (I am not sure whether there was also a flow criterion, but I do not think so). I think training on the original full set should still work well, maybe a bit slower.

IIRC "extremely hard" samples have disparity magnitudes > 300 pixels (I am not sure whether there was also a flow criterion, but I do not think so). I think training on the original full set should still work well, maybe a bit slower.

I traversed the Flything3D subset, and the subset still has a few samples containing more than 25% of the pixels, which disparity magnitudes > 300。This makes me more curious about the screening.