Binaries/Code Datasets Open Source Software

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

Generated Matching Dataset

Terms of use

The dataset is only provided for research purposes and without any warranty. Any commercial use is prohibited. When using, you should cite the following paper:

P.Fischer, A. Dosovitskiy, T. Brox
Descriptor Matching with Convolutional Neural Networks: a Comparison to SIFT,
on arXiv(1405.5769), May 2014.

Link to dataset: Dataset download

The file also includes MATLAB code to process the ground truth of the geometric transformations. Given the coordinates in one image of a pair, they can be mapped to the other using the applyTransformationToCoords() function. Pairs are always made from the _original image and a warped image. Hence the ground truth in blur2.mat corresponds to the pair _original.jpg / blur2.jpg.