Uni-Logo
 

Binaries/Code

Binaries/Code Datasets Open Source Software

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.


Spectral Graph Reduction

Matlab Code coming soon

F. Galasso, Margret Keuper, Thomas Brox, B. Schiele
Spectral graph reduction for efficient image and streaming video segmentation,
IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 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.


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.


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.


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)

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, preprint 2013.

P. Ochs, T. Brox
Higher order motion models and spectral clustering,
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), Crete, Greece, Springer, LNCS, Sept. 2010.


Dense Point Tracking

Download Executable for 64-bit Linux
Download Executable for Nvidia GPUs (requires CUDA 5.0)
Download Executable for Nvidia GPUs (requires CUDA 3.2)

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-OS
Download Matlab Mex-functions for 64-bit Linux, 32-bit and 64-bit Windows, and 64-bit Mac-OS

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

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.