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Freiburg-Berkeley Motion Segmentation Dataset
Video Segmentation Benchmark
Image Sequences
TEM Dataset
TILDA Textile Texture Database
Training data for Exemplar CNN
Generated Matching Dataset
Training data for chair generation
Stereo Ego-motion Dataset
Optical Flow Datasets: "Flying Chairs", "ChairsSDHom"
Scene Flow Datasets
Human Part Segmentation Datasets  
Rendered Handpose Dataset
Pedestrian Zone Scene


Optical Flow Datasets: FlyingChairs, ChairsSDHom


The "Flying Chairs" Dataset

The "Flying Chairs" are a synthetic dataset with optical flow ground truth. It consists of 22872 image pairs and corresponding flow fields. Images show renderings of 3D chair models moving in front of random backgrounds from Flickr. Motions of both the chairs and the background are purely planar.


This dataset has been used for training convolutional networks in the ICCV 2015 paper FlowNet: Learning Optical Flow with Convolutional Networks.



Terms of Use

The dataset is provided for research purposes only and without any warranty. Any commercial use is prohibited. If you use the dataset in your research, you should cite the aforementioned paper.


@InProceedings{DFIB15,
  author    = "A. Dosovitskiy and P. Fischer and E. Ilg and P. H{\"a}usser and C. Haz{\i}rba{\c{s}} and V. Golkov and P. v.d. Smagt and D. Cremers and T. Brox",
  title     = "FlowNet: Learning Optical Flow with Convolutional Networks",
  booktitle = "IEEE International Conference on Computer Vision (ICCV)",
  month     = " ",
  year      = "2015",
  url       = "http://lmb.informatik.uni-freiburg.de/Publications/2015/DFIB15"
}

Download the "Flying Chairs" dataset


"Flying chairs" train-validation split  (1 - train, 2 - validation)


Sintel train-validation split  (1 - train, 2 - validation)


Download handy Python IO routines




The "Flying Chairs 2" Dataset

The Flying Chairs 2 dataset is generated using the same settings as the Flying Chairs dataset, but contains additional modalities that were used to train the networks in the paper Occlusions, Motion and Depth Boundaries with a Generic Network for Disparity, Optical Flow or Scene Flow Estimation.


All modalities provided are:

  • First and second image (suffixes _img_0 and _img_1)
  • First and second image object IDs (suffixes _oids_0 and _oids_1)
  • Forward and backward optical flow (suffixes _flow_01 and _flow_10)
  • Forward and backward occlusions (suffixes _occ_01 and _occ_10)
  • Forward and backward occlusion weights (computed according to the paper; suffixes _occ_weights_01 and _occ_weights_10)
  • Forward and backward motion boundaries (suffixes _mb_01 and _mb_10)
  • Forward and backward motion boundary weights (computed according to the paper; suffixes _mb_weights_01 and _mb_weights_10)

Terms of Use

The dataset is provided for research purposes only and without any warranty. Any commercial use is prohibited. If you use the dataset in your research, you should cite the following two papers:


@InProceedings{DFIB15,
  author    = "A. Dosovitskiy and P. Fischer and E. Ilg and P. H{\"a}usser and C. Haz{\i}rba{\c{s}} and V. Golkov and P. v.d. Smagt and D. Cremers and T. Brox",
  title     = "FlowNet: Learning Optical Flow with Convolutional Networks",
  booktitle = "IEEE International Conference on Computer Vision (ICCV)",
  month     = " ",
  year      = "2015",
  url       = "http://lmb.informatik.uni-freiburg.de/Publications/2015/DFIB15"
}
@InProceedings{ISKB18,
  author    = "E. Ilg and T. Saikia and M. Keuper and T. Brox",
  title     = "Occlusions, Motion and Depth Boundaries with a Generic Network for Disparity, Optical Flow or Scene Flow Estimation",
  booktitle = "European Conference on Computer Vision (ECCV)",
  month     = " ",
  year      = "2018",
  url       = "http://lmb.informatik.uni-freiburg.de/Publications/2018/ISKB18"
}

Download the "Flying Chairs 2" dataset


Download handy Python IO routines




The "ChairsSDHom" Dataset

"ChairsSDHom" is a synthetic dataset with optical flow ground truth. Designed to be robust to untextured regions and to produce flow magnitude histograms close to those of the UCF101 dataset, ChairsSDHom is a good candidate for training if you want your optical flow method to work well on real-world data and generally rather small displacements. The dataset is abstract enough to not overfit to any realistic scenario, so even if you have specialized data, ChairsSDHom may still serve as good additional or pretraining data.


This dataset has been used for training convolutional networks in the CVPR 2017 paper FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks.



Terms of Use

The dataset is provided for research purposes only and without any warranty. Any commercial use is prohibited. If you use the dataset in your research, you should cite the aforementioned paper.


@InProceedings{IMKDB17,
  author    = "E. Ilg and N. Mayer and T. Saikia and M. Keuper and A. Dosovitskiy and T. Brox",
  title     = "FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks",
  booktitle = "IEEE Conference on Computer Vision and Pattern Recognition (CVPR)",
  month     = "Jul",
  year      = "2017",
  url       = "http://lmb.informatik.uni-freiburg.de//Publications/2017/IMKDB17"
}

Download the "ChairsSDHom" dataset


Download handy Python IO routines



The "ChairsSDHom extended" Dataset

The extended version contains the same flows and images, but also additional modalities that were used to train the networks in the paper Occlusions, Motion and Depth Boundaries with a Generic Network for Disparity, Optical Flow or Scene Flow Estimation.


All modalities provided are:

  • First and second image (suffixes _img_0 and _img_1)
  • First and second image object IDs (suffixes _oids_0 and _oids_1)
  • Forward optical flow (suffix _flow_01)
  • Forward occlusions (suffix _occ_01)
  • Forward occlusion weights (computed according to the paper; suffix _occ_weights_01)
  • Forward motion boundaries (suffix _mb_01)
  • Forward motion boundary weights (computed according to the paper; suffix _mb_weights_01)

Terms of Use

The dataset is provided for research purposes only and without any warranty. Any commercial use is prohibited. If you use the dataset in your research, you should cite the following two papers:


@InProceedings{IMKDB17,
  author    = "E. Ilg and N. Mayer and T. Saikia and M. Keuper and A. Dosovitskiy and T. Brox",
  title     = "FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks",
  booktitle = "IEEE Conference on Computer Vision and Pattern Recognition (CVPR)",
  month     = "Jul",
  year      = "2017",
  url       = "http://lmb.informatik.uni-freiburg.de//Publications/2017/IMKDB17"
}
@InProceedings{ISKB18,
  author    = "E. Ilg and T. Saikia and M. Keuper and T. Brox",
  title     = "Occlusions, Motion and Depth Boundaries with a Generic Network for Disparity, Optical Flow or Scene Flow Estimation",
  booktitle = "European Conference on Computer Vision (ECCV)",
  month     = " ",
  year      = "2018",
  url       = "http://lmb.informatik.uni-freiburg.de/Publications/2018/ISKB18"
}

Download the "ChairsSDHom extended" dataset


Download handy Python IO routines



Contact

For questions, please contact Nikolaus Mayer ().