Cross-Scale Cost Aggregation Code

Cross-Scale Cost Aggregation for Stereo Matching (CVPR 2014) ================ ## Compilation ### Windows The code is a Visual Studio 2010 project on Windows x64 platform. To build the project, you need to configure [OpenCV](http://opencv.org/) on your own PC. (version 2.4.6, however, other versions are acceptable by modifying [CommFunc.h](/SSCA/CommFunc.h)). ### Other Platforms The code requires no platform-dependent libraries. Thus, it is easy to compile it on other platforms with OpenCV. ## Usage 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 + gradient](#GF) - `CEN` -- [Census Transform](#CT) - `CG` -- Census + gradient - `[CA_METHOD]` -- cost aggregation methods, currently support: - `GF` -- [guided image filter](#GF) - `BF` -- [bilateral filter](#BF) - `BOX` -- box filter - `NL` -- [non-local cost aggregation](#NL) - `ST` -- [segment-tree cost aggregation](#ST) - `[PP_METHOD]` -- post processing methods, currently support: - `WM` -- [weighted median filtering](#GF) - `SG` -- 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. `60` for Middlebury and `256` for KITTI dataets. - `[disSc]` -- scale disparity, e.g. `4` for Middlebury and `1` for KITTI datasets. **Hint**: to enable post-processing, you must uncomment `// #define COMPUTE_RIGHT` in [CommFunc.h](/SSCA/CommFunc.h) to allow computing right disparity map. ## Citation 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](#GF), [NL](#NL), [ST](#ST)) are built uppon other papers' code, you also need to cite corresponding papers as listed below. ## Reference <a name="CT">[CT]</a>: R. Zabih and J. Woodfill. Non-parametric local transforms for computing visual correspondence. In ECCV, 1994 <a name="GF">[GF]</a>: C. Rhemann, A. Hosni, M. Bleyer, C. Rother, and M. Gelautz. Fast cost-volume filtering for visual correspondence and beyond. In CVPR, 2011 <a name="ST">[ST]</a>: X. Mei, X. Sun, W. Dong, H. Wang, and X. Zhang. Segment-tree based cost aggregation for stereo matching. In CVPR, 2013 <a name="BF">[BF]</a>: K.-J. Yoon and I. S. Kweon. Adaptive support-weight approach for correspondence search. TPAMI, 2006 <a name="NL">[NL]</a>: Q. Yang. A non-local cost aggregation method for stereo matching. In CVPR, 2012