driving__frames_finalpass_webp.tar
Nikolaus Mayer and Eddy Ilg and Philip Hausser and Philipp Fischer and Daniel Cremers and Alexey Dosovitskiy and Thomas Brox

driving__frames_finalpass_webp.tar970.71MB
Type: Dataset
Tags: Dataset, disparity, scene flow, synthetic, optical flow

Bibtex:
@article{,
title= {driving__frames_finalpass_webp.tar},
keywords= {Dataset, optical flow, synthetic, disparity, scene flow},
journal= {},
author= {Nikolaus Mayer and Eddy Ilg and Philip Hausser and Philipp Fischer and Daniel Cremers and Alexey Dosovitskiy and Thomas Brox},
year= {},
url= {https://lmb.informatik.uni-freiburg.de/resources/datasets/SceneFlowDatasets.en.html},
license= {},
abstract= {This torrent contains the "Final pass" images (WebP format) for the "Driving" dataset from the CVPR 2016 paper "A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation" by Mayer et al. (https://lmb.informatik.uni-freiburg.de/resources/datasets/SceneFlowDatasets.en.html).},
superseded= {},
terms= {https://lmb.informatik.uni-freiburg.de/resources/datasets/SceneFlowDatasets.en.html#tou}
}

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Average Speed 932.48kB/s
Best Time 17 mins, 21 secs
Best Speed 932.48kB/s
Worst Time 17 mins, 21 secs
Worst Speed 932.48kB/s
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