Code for the two papers [1] and [2].

The code is written in Matlab and use mex'ed c++ code for the optimization parts.

Example result: Two input images and the estimated disparity map.

The code has been tested on MATLAB 2013a with GCC 4.8 on Ubuntu 13.10.

Requirements

1. MATLAB and a c++ compiler.

Getting started

1. Run mex -setup.

• In example_global.m Figure 4b) from [1] is reproduced.
• In example_simultanouse.m Figure 4 from [2] is reproduced.
• In example_ncc we show how to use a unary term based on normalized cross correlation.

Third party software

The code is bundled with optimization software.

• To perform binary fusion we use roof duality [3].

• To perform simultaneous fusion we use TRW-S [4].

• The unary and pairwise cost of dispmap_globalstereo is based on [5].

Extend

The best way to define a new unary (or any other modifications) is to inherit from dispmap_super. This is how dispmap_ncc and dispmap_globalstereo is implemented.

Note: Simultaneous Fusion Moves will only work with pairwise cost of the form \alpha(p,q) |p-q|^kernel where kernel\in{1,2}. Overloading the pairwise_cost method with a pairwise_cost function of a different from will break simultaneous fusion. Binary fusions can however still be performed.

References

1. In Defense of 3D-Label Stereo.
   IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013.
   Carl Olsson, Johannes Ulén, and Yuri Boykov.

2. Simultaneous Fusion Moves for 3D-Label Stereo.
   Intentional Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), 2013.
   Johannes Ulén and Carl Olsson

3. Optimizing binary MRFs via extended roof duality.
   IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2007.
   C. Rother, V. Kolmogorov, V. Lempitsky, and M. Szummer.

4. Convergent Tree-reweighted Message Passing for Energy Minimization.
   IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2006.
   Vladimir Kolmogorov.

5. Global Stereo Reconstruction under Second-Order Smoothness Priors.
   IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2009.
   Olivier Woodford, Phil Torr, Ian Reid, Andrew Fitzgibbon.