Binaries/Code
We provide binaries and source code of some selected works in order to help other researchers to compare their results or to use our work as a module for their research. Please understand that we can only provide what is offered here. E-Mails requesting other free code will be ignored.
Terms of use
All code is provided for research purposes only and without any warranty. Any commercial use requires our consent. When using the code in your research work, you should cite the respective paper. Refer to the readme file in each package to learn how to use the program.FLN-EPN-RPN
Source code (GitHub)
FIT: Freiburg Imra Testing dataset.
nuScenes: nuScenes post-processed testing dataset, originally obtained from nuscenes website
Waymo post-processed testing dataset can be downloaded from link
Trained models
O. Makansi and O. Cicek and K. Buchicchio and T. Brox
Multimodal Future Localization and Emergence Prediction for Objects in Egocentric View with a Reachability Prior,
IEEE Conference in Computer Vision and Pattern Recognition (CVPR), 2020
FIT: Freiburg Imra Testing dataset.
nuScenes: nuScenes post-processed testing dataset, originally obtained from nuscenes website
Waymo post-processed testing dataset can be downloaded from link
Trained models
O. Makansi and O. Cicek and K. Buchicchio and T. Brox
Multimodal Future Localization and Emergence Prediction for Objects in Egocentric View with a Reachability Prior,
IEEE Conference in Computer Vision and Pattern Recognition (CVPR), 2020
Multimodal Future Prediction
Source code (GitHub)
Processed SDD dataset: Train and Test. The original dataset can be obtained from SDD
Trained models
O. Makansi and E. Ilg and O. Cicek and T. Brox
Overcoming Limitations of Mixture Density Networks: A Sampling and Fitting Framework for Multimodal Future Prediction,
IEEE Conference in Computer Vision and Pattern Recognition (CVPR), 2019
Processed SDD dataset: Train and Test. The original dataset can be obtained from SDD
Trained models
O. Makansi and E. Ilg and O. Cicek and T. Brox
Overcoming Limitations of Mixture Density Networks: A Sampling and Fitting Framework for Multimodal Future Prediction,
IEEE Conference in Computer Vision and Pattern Recognition (CVPR), 2019
FlowNet 3.0 + DispNet 3.0 + FlowNetH
We publish the code on GitHub:
netdef_models (GitHub)
Please see README.md for instructions on how to download data, models and code.
E. Ilg, T. Saikia, M. Keuper, T. Brox
Occlusions, Motion and Depth Boundaries with a Generic Network for Disparity, Optical Flow or Scene Flow Estimation,
European Conference on Computer Vision (ECCV), 2018.
E. Ilg, Ö. Çiçek, S. Galesso, A. Klein, O. Makansi, F. Hutter, T. Brox
Uncertainty Estimates and Multi-Hypotheses Networks for Optical Flow
European Conference on Computer Vision (ECCV), 2018.
netdef_models (GitHub)
Please see README.md for instructions on how to download data, models and code.
E. Ilg, T. Saikia, M. Keuper, T. Brox
Occlusions, Motion and Depth Boundaries with a Generic Network for Disparity, Optical Flow or Scene Flow Estimation,
European Conference on Computer Vision (ECCV), 2018.
E. Ilg, Ö. Çiçek, S. Galesso, A. Klein, O. Makansi, F. Hutter, T. Brox
Uncertainty Estimates and Multi-Hypotheses Networks for Optical Flow
European Conference on Computer Vision (ECCV), 2018.
FlowNet 2.0
Complete source code is availalbe here:
flownet2 (GitHub)
Please see README.md for instructions on how to download data and models.
Dockerfile for easy installation of the complete code in one step (requires Docker):
flownet2-docker (GitHub)
E. Ilg, N. Mayer, T. Saikia, M. Keuper, A. Dosovitskiy, T. Brox
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks,
IEEE Conference in Computer Vision and Pattern Recognition (CVPR), 2017.
flownet2 (GitHub)
Please see README.md for instructions on how to download data and models.
Dockerfile for easy installation of the complete code in one step (requires Docker):
flownet2-docker (GitHub)
E. Ilg, N. Mayer, T. Saikia, M. Keuper, A. Dosovitskiy, T. Brox
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks,
IEEE Conference in Computer Vision and Pattern Recognition (CVPR), 2017.
Multi-view 3D Models from Single Images with a Convolutional Network
Source code (GitHub)
Pre-rendered test set
Trained models
M. Tatarchenko, A. Dosovitskiy, T. Brox
Multi-view 3D Models from Single Images with a Convolutional Network,
European Conference on Computer Vision (ECCV), 2016
Pre-rendered test set
Trained models
M. Tatarchenko, A. Dosovitskiy, T. Brox
Multi-view 3D Models from Single Images with a Convolutional Network,
European Conference on Computer Vision (ECCV), 2016
Generating Images with Perceptual Similarity Metrics based on Deep Networks
v0.5:
Testing and training code
Custom caffe version (for training): https://github.com/dosovits/caffe-fr-chairs (deepsim branch)
v0: Trained models for layers pool5-fc8 and a python demo Trained models for layers norm1-conv4
A. Dosovitskiy, T. Brox
Generating Images with Perceptual Similarity Metrics based on Deep Networks,
Advances in Neural Information Processing Systems (NIPS), 2016.
Custom caffe version (for training): https://github.com/dosovits/caffe-fr-chairs (deepsim branch)
v0: Trained models for layers pool5-fc8 and a python demo Trained models for layers norm1-conv4
A. Dosovitskiy, T. Brox
Generating Images with Perceptual Similarity Metrics based on Deep Networks,
Advances in Neural Information Processing Systems (NIPS), 2016.
Efficient and Robust Networks for Semantic Segmentation full code
Download modified master branch Caffe: Download Caffe_FASTv1.0 (Modified Caffe + models + brief readme)
Please read the included FastNet_README.md file.
Augmentation scripts comming soon.
G. L. Oliveira, W. Burgard, T. Brox
Efficient and Robust Deep Networks for Semantic Segmentation,
G. L. Oliveira, W. Burgard, T. Brox
Efficient Deep Methods for Monocular Road Segmentation,
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2016.
Please read the included FastNet_README.md file.
Augmentation scripts comming soon.
G. L. Oliveira, W. Burgard, T. Brox
Efficient and Robust Deep Networks for Semantic Segmentation,
G. L. Oliveira, W. Burgard, T. Brox
Efficient Deep Methods for Monocular Road Segmentation,
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2016.
Disp- and FlowNet: Full code for testing and training networks
Download modified master branch Caffe: Download v1.2 (Modified Caffe + models + brief readme, LMDB scaling bug fixed, FlowNetC model included)
Please read the included DISPNET-README.md file.
N. Mayer, E. Ilg, P. Häusser, P. Fischer, D. Cremers, A. Dosovitskiy, T. Brox
A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation,
IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
A. Dosovitskiy and P. Fischer and E. Ilg and P. Häusser and C. Hazirbas and V. Golkov and P. v.d. Smagt and D. Cremers and T. Brox
FlowNet: Learning Optical Flow with Convolutional Networks,
IEEE International Conference on Computer Vision (ICCV), 2015.
Earlier versions:
DispNet and FlowNet v1.0 (LMDB scaling fixed)
DispNet and FlowNet v1.0 (LMDB scaling bug)
DispNet 0.5
FlowNet 0.1
FlowNet 1.0
FlowNet small displacements model
Please read the included DISPNET-README.md file.
N. Mayer, E. Ilg, P. Häusser, P. Fischer, D. Cremers, A. Dosovitskiy, T. Brox
A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation,
IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
A. Dosovitskiy and P. Fischer and E. Ilg and P. Häusser and C. Hazirbas and V. Golkov and P. v.d. Smagt and D. Cremers and T. Brox
FlowNet: Learning Optical Flow with Convolutional Networks,
IEEE International Conference on Computer Vision (ICCV), 2015.
Earlier versions:
DispNet and FlowNet v1.0 (LMDB scaling fixed)
DispNet and FlowNet v1.0 (LMDB scaling bug)
DispNet 0.5
FlowNet 0.1
FlowNet 1.0
FlowNet small displacements model
Motion Trajectory Segmentation via Minimum Cost Multicuts
Download Executables for 64-bit Linux
M. Keuper, B. Andres, T. Brox
Motion Trajectory Segmentation via Minimum Cost Multicuts,
IEEE International Conference on Computer Vision (ICCV), 2015.
M. Keuper, B. Andres, T. Brox
Motion Trajectory Segmentation via Minimum Cost Multicuts,
IEEE International Conference on Computer Vision (ICCV), 2015.
Global, Dense Multiscale Reconstruction for a Billion Points
Download Executables for 64-bit Linux
Project page
B. Ummenhofer, T. Brox
Global, Dense Multiscale Reconstruction for a Billion Points,
IEEE International Conference on Computer Vision (ICCV), 2015.
Project page
B. Ummenhofer, T. Brox
Global, Dense Multiscale Reconstruction for a Billion Points,
IEEE International Conference on Computer Vision (ICCV), 2015.
Inverting Visual Representations with Convolutional Networks
Code and examples for new Caffe version (2016):
training config and a trained model for reconstruction from FC6
To be used with this Caffe https://github.com/dosovits/caffe-fr-chairs
Download modified Caffe used in the paper:
modified Caffe + brief readme
A. Dosovitskiy and T. Brox
Inverting Visual Representations with Convolutional Networks,
IEEE Conference in Computer Vision and Pattern Recognition (CVPR), 2016.
Learning to Generate Chairs with Convolutional Neural Networks
New version in new Caffe (2016)
trained model, demo, training example
To be used with this Caffe https://github.com/dosovits/caffe-fr-chairs
Version used for the CVPR paper
modified Caffe + models + matlab scripts
trained model
training data
A. Dosovitskiy, J. T. Springenberg and T. Brox
Learning to Generate Chairs with Convolutional Neural Networks,
IEEE Conference in Computer Vision and Pattern Recognition (CVPR), 2015.
Scene flow from RGB-D sequences
Download C++ Code
J. Quiroga, Thomas Brox, F. Devernay, J. Crowley
Dense semi-rigid scene flow estimation from RGBD images,
European Conference on Computer Vision (ECCV), 2014.
J. Quiroga, Thomas Brox, F. Devernay, J. Crowley
Dense semi-rigid scene flow estimation from RGBD images,
European Conference on Computer Vision (ECCV), 2014.
Exemplar Convolutional Neural Networks
Download Code for Linux
A. Dosovitskiy, J. T. Springenberg, M. Riedmiller and T. Brox
Discriminative Unsupervised Feature Learning with Convolutional Neural Networks,
Advances in Neural Information Processing Systems 27 (NIPS), 2014.
A. Dosovitskiy, P.Fischer, J. T. Springenberg, M. Riedmiller and T. Brox
Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks,
IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2015.
A. Dosovitskiy, J. T. Springenberg, M. Riedmiller and T. Brox
Discriminative Unsupervised Feature Learning with Convolutional Neural Networks,
Advances in Neural Information Processing Systems 27 (NIPS), 2014.
A. Dosovitskiy, P.Fischer, J. T. Springenberg, M. Riedmiller and T. Brox
Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks,
IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2015.
Image Descriptors based on Curvature Histograms
Download Code for Linux (contains code for combination with HOG and SIFT)
The download provides feature computation code for integration with the Felzenszwalb DPM code and for integration with the VLfeat framework.
P. Fischer, T. Brox
Image Descriptors based on Curvature Histograms,
German Conference on Pattern Recognition (GCPR), 2014.
The download provides feature computation code for integration with the Felzenszwalb DPM code and for integration with the VLfeat framework.
P. Fischer, T. Brox
Image Descriptors based on Curvature Histograms,
German Conference on Pattern Recognition (GCPR), 2014.
Point-Based Reconstruction
Download Executable for 64-bit Linux (requires CUDA 5.5)
B. Ummenhofer, T. Brox
Point-Based 3D Reconstruction of Thin Objects,
IEEE International Conference on Computer Vision (ICCV), 2013.
B. Ummenhofer, T. Brox
Point-Based 3D Reconstruction of Thin Objects,
IEEE International Conference on Computer Vision (ICCV), 2013.
Non-smooth Non-convex Optimization
Download Matlab Code
P. Ochs, A. Dosovitskiy, T. Brox, T. Pock
An iterated L1 Algorithm for Non-smooth Non-convex Optimization in Computer Vision,
Conference on Computer Vision and Pattern Recognition (CVPR), 2013.
P. Ochs, A. Dosovitskiy, T. Brox, T. Pock
An iterated L1 Algorithm for Non-smooth Non-convex Optimization in Computer Vision,
Conference on Computer Vision and Pattern Recognition (CVPR), 2013.
Dense Label Interpolation
Download Executable for 64-bit Linux
P. Ochs, T. Brox
Object Segmentation in Video: A Hierarchical Variational Approach for Turning Point Trajectories into Dense Regions,
IEEE International Conference on Computer Vision (ICCV), 2011.
P. Ochs, T. Brox
Object Segmentation in Video: A Hierarchical Variational Approach for Turning Point Trajectories into Dense Regions,
IEEE International Conference on Computer Vision (ICCV), 2011.
Motion Segmentation
Download Executable for 64-bit Linux (improved pairwise model + densify, PAMI 2013)
Download Code for 64-bit Linux (optical flow variation as used in the definition of the pairwise affinities, PAMI 2013)
Download Executable for 64-bit Linux (higher order, CVPR 2012)
Download Executable for 64-bit Linux (pairwise model, ECCV 2010)
Download Source code (pairwise model, ECCV 2010)
These downloads provide executables with one example video. See the Freiburg Berkeley Motion Segmentation Dataset for the complete dataset.
P. Ochs, J. Malik, T. Brox
Segmentation of moving objects by long term video analysis,
IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(6):1187-1200, Jun. 2014.
P. Ochs, T. Brox
Higher order motion models and spectral clustering,
International Conference on Computer Vision and Pattern Recognition (CVPR), 2012.
T. Brox, J. Malik
Object segmentation by long term analysis of point trajectories,
European Conference on Computer Vision (ECCV), Springer, LNCS, Sept. 2010.
Download Code for 64-bit Linux (optical flow variation as used in the definition of the pairwise affinities, PAMI 2013)
Download Executable for 64-bit Linux (higher order, CVPR 2012)
Download Executable for 64-bit Linux (pairwise model, ECCV 2010)
Download Source code (pairwise model, ECCV 2010)
These downloads provide executables with one example video. See the Freiburg Berkeley Motion Segmentation Dataset for the complete dataset.
P. Ochs, J. Malik, T. Brox
Segmentation of moving objects by long term video analysis,
IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(6):1187-1200, Jun. 2014.
P. Ochs, T. Brox
Higher order motion models and spectral clustering,
International Conference on Computer Vision and Pattern Recognition (CVPR), 2012.
T. Brox, J. Malik
Object segmentation by long term analysis of point trajectories,
European Conference on Computer Vision (ECCV), Springer, LNCS, Sept. 2010.
Dense Point Tracking
Download Code with optical flow library for 64-bit Linux
Download Code with optical flow library for Nvidia GPUs (requires CUDA 7.5)
N. Sundaram, T. Brox, K. Keutzer
Dense point trajectories by GPU-accelerated large displacement optical flow,
European Conference on Computer Vision (ECCV), Crete, Greece, Springer, LNCS, Sept. 2010.
Download Code with optical flow library for Nvidia GPUs (requires CUDA 7.5)
N. Sundaram, T. Brox, K. Keutzer
Dense point trajectories by GPU-accelerated large displacement optical flow,
European Conference on Computer Vision (ECCV), Crete, Greece, Springer, LNCS, Sept. 2010.
Large Displacement Optical Flow
Download Executable for 64-bit Linux
Download C++ Library for 64-bit Linux
Download Executable for 64-bit Mac-OS
Download C++ Library for 64-bit Mac-OSX 10.9 (problems with OSX 10.10)
Download Matlab Mex-functions for 64-bit Linux, 32-bit and 64-bit Windows, and 64-bit Mac-OS
Download Source code
T. Brox, J. Malik
Large displacement optical flow: descriptor matching in variational motion estimation,
IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(3):500-513, March 2011.
Download C++ Library for 64-bit Linux
Download Executable for 64-bit Mac-OS
Download C++ Library for 64-bit Mac-OSX 10.9 (problems with OSX 10.10)
Download Matlab Mex-functions for 64-bit Linux, 32-bit and 64-bit Windows, and 64-bit Mac-OS
Download Source code
T. Brox, J. Malik
Large displacement optical flow: descriptor matching in variational motion estimation,
IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(3):500-513, March 2011.
Classical Variational Optical Flow
Download Executable for 64-bit Linux
Download C++ Library for 64-bit Linux
Download Executable for 32-bit Windows
Download Matlab Mex-functions for 64-bit Linux, 32-bit and 64-bit Windows
Download Source code (special case of large displacement optical flow)
The code is not exactly identical to the work described in the original ECCV 2004 paper. The Windows executable is less efficient and uses an outdated output file format. If you have access to a Linux machine or Matlab, I recommend using these versions.
T. Brox, A. Bruhn, N. Papenberg, J. Weickert
High accuracy optical flow estimation based on a theory for warping,
T. Pajdla and J. Matas (Eds.), European Conference on Computer Vision (ECCV) Prague, Czech Republic, Springer, LNCS, Vol. 3024, 25-36, May 2004.
©Springer-Verlag Berlin Heidelberg 2004
(bibtex)
Download C++ Library for 64-bit Linux
Download Executable for 32-bit Windows
Download Matlab Mex-functions for 64-bit Linux, 32-bit and 64-bit Windows
Download Source code (special case of large displacement optical flow)
The code is not exactly identical to the work described in the original ECCV 2004 paper. The Windows executable is less efficient and uses an outdated output file format. If you have access to a Linux machine or Matlab, I recommend using these versions.
T. Brox, A. Bruhn, N. Papenberg, J. Weickert
High accuracy optical flow estimation based on a theory for warping,
T. Pajdla and J. Matas (Eds.), European Conference on Computer Vision (ECCV) Prague, Czech Republic, Springer, LNCS, Vol. 3024, 25-36, May 2004.
©Springer-Verlag Berlin Heidelberg 2004
(bibtex)
Nonlocal means with cluster trees
Download Executables for 64-bit Linux
The program runs the non-iterative method described in the paper using no overlap for the cluster tree.
T. Brox, O. Kleinschmidt, D. Cremers
Efficient nonlocal means for denoising of textural patterns,
IEEE Transactions on Image Processing 17(7):1083-1092, July 2008.
The program runs the non-iterative method described in the paper using no overlap for the cluster tree.
T. Brox, O. Kleinschmidt, D. Cremers
Efficient nonlocal means for denoising of textural patterns,
IEEE Transactions on Image Processing 17(7):1083-1092, July 2008.