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CMP Stereo Algorithm Evaluation

Center for Machine Perception
Czech Technical University Prague
http://cmp.felk.cvut.cz/
  Jana Kostliva   Radim Sara   Jan Cech  

Introduction Results Download Upload

Introduction

This website presents material for stereo matching (dense or semi-dense) algorithm evaluation described in [1,2]. It contains matlab toolbox for evaluating your algorithm, comparison of already tested algorithms, downloads with scenes or results and upload form for uploading your results.

Our methodology evaluates algorithms over a wide range of parameter settings which allows to get the algorithm performance as good as possible. The results are presented as error-plots as well as numerical characteristics describing the algorithm performance.

Our evaluation uses also scenes from Middlebury dataset (Cones, Teddy, Tsukuba, and Venus), courtesy of D. Scharstein, which has been introduced in [3,4].

We invite other researches to contribute their results to this page to create wider a study. To this end, we provide a matlab toolbox which helps you to run the evaluation on your algorithm. If you are interested in contributing, you can download it on page "Downloads".


News:


The website has four main pages:


How to cite:

We give permission to use and publish all images and results on this website. Nevertheless, if you use our toolbox and/or results created based on this method, please cite the appropriate articles [1,2].


References:

[1]  J. Kostlivá, J.Čech, R.©ára: Feasibility Boundary in Dense and Semi-Dense Stereo Matching. In BenCOS2007, in conjunction with CVPR2007, on-line, 2007. Best Paper Award
[2] J. Kostlivá, J.Cech, R.©ára: ROC Based Evaluation of Stereo Algorithms. Research report CTU-CMP-2007-08, Center for Machine Perception, K13133 FEE, Czech Technical University, Prague, Czech Republic, 2007.
[3] D. Scharstein, R. Szeliski: A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms. IJCV 47(1/2/3):7-42, 2002.
[4] D. Scharstein, R. Szeliski: High-accuracy Stereo Depth Maps Using Structured Light. In CVPR 2003, pages 195-202, 2002.


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