The code is a Visual Studio 2010 project on Windows x64 platform. To build the project, you need to configure OpenCV on your own PC. (version 2.4.6, however, other versions are acceptable by modifying CommFunc.h).
The code requires no platform-dependent libraries. Thus, it is easy to compile it on other platforms with OpenCV.
Run the program with the following paramters:
Usage: [CC_METHOD] [CA_METHOD] [PP_METHOD] [C_ALPHA] [lImg] [rImg] [lDis] [maxDis] [disSc]
[CC_METHOD]-- cost computation methods, currently support:GRD-- intensity + gradientCEN-- Census TransformCG-- Census + gradient
[CA_METHOD]-- cost aggregation methods, currently support:GF-- guided image filterBF-- bilateral filterBOX-- box filterNL-- non-local cost aggregationST-- segment-tree cost aggregation
[PP_METHOD]-- post processing methods, currently support:WM-- weighted median filteringSG-- segment based (experimental)
[C_ALPHA]-- regularization paramter, i.e.$\lambda$in the paper.[lImg]-- input left color image file name. (all formats supported by OpenCV)[rImg]-- input right color image file name.[lDis]-- output left disparity map file name.[maxDis]-- maximum disparity range, e.g.60for Middlebury and256for KITTI dataets.[disSc]-- scale disparity, e.g.4for Middlebury and1for KITTI datasets.
Hint: to enable post-processing, you must uncomment // #define COMPUTE_RIGHT in CommFunc.h to allow computing right disparity map.
Citation is very important for researchers. If you find this code useful, please cite:
@inproceedings{CrossScaleStereo,
author = {Kang Zhang and Yuqiang Fang and Dongbo Min and Lifeng Sun and Shiqiang Yang and Shuicheng Yan and Qi Tian},
title = {Cross-Scale Cost Aggregation for Stereo Matching},
booktitle = {CVPR},
year = {2014}
}
Since some cost aggregation methods (GF, NL, ST) are built uppon other papers' code, you also need to cite corresponding papers as listed below.
[CT]: R. Zabih and J. Woodfill. Non-parametric local transforms for computing visual correspondence. In ECCV, 1994
[GF]: C. Rhemann, A. Hosni, M. Bleyer, C. Rother, and M. Gelautz. Fast cost-volume filtering for visual correspondence and beyond. In CVPR, 2011
[ST]: X. Mei, X. Sun, W. Dong, H. Wang, and X. Zhang. Segment-tree based cost aggregation for stereo matching. In CVPR, 2013
[BF]: K.-J. Yoon and I. S. Kweon. Adaptive support-weight approach for correspondence search. TPAMI, 2006
[NL]: Q. Yang. A non-local cost aggregation method for stereo matching. In CVPR, 2012
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