Sped-up PatchMatch Belief Propagation (SPM-BP)

This is an implementation of SPM-BP for Optical Flow estimation that correspondes to our published paper:

Y. Li, D. Min, M. S. Brown, M. N. Do, J. Lu. "SPM-BP: Sped-up PatchMatch Belief Propagation for Continuous MRFs". in ICCV 2015.

Project Website: [Efficient Inference for Continuous MRFs] (https://publish.illinois.edu/visual-modeling-and-analytics/efficient-inference-for-continuous-mrfs/)

Usage

• The whole codes are in the code folder. You can use CMake to compile SPM-BP (Tested only on 64 bit Windows 7 with Visual Studio 2012; but the code should be able to run in Linux or Mac with slight modification).
• For windows user, a compiled execuable with demo usage is provided in Release folder.
• We will be happy if you cite us when using this code!
• If you want to test Stereo Matching using SPM-BP, we can share the execuable upon request.

Dependency

• OpenCV 3.0
• SLIC superpixel [1] (included)
• [Cross-based Local Multipoint Filtering (CLMF) [2]] (https://sites.google.com/site/filteringtutorial/) (included)
• Fast Global Image Smoothing (FGS) [3] (modified and included)

References

[1] R. Achanta , A. Shaji, K. Smith, A. Lucchi,P. Fua, and S. Susstrunk, " SLIC superpixels compared to state-of-the-art superpixel methods," IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), 34(11), 2274-2282, 2012.

[2] J. Lu, K. Shi, D. Min, L. Lin, and M. N. Do, "Cross-based local multipoint filtering," in Proc. IEEE Int. Conf. Computer Vision and Pattern Recognition (CVPR), Providence, Rhode Island, 2012.

[3] D. Min, S. Choi, J. Lu, B. Ham, K. Sohn, and M. N. Do, “Fast Global Image Smoothing Based on Weighted Least Squares,” IEEE Trans. on Image Processing (TIP), 23(12), 5638-5653, 2014.

License

Copyright (c) 2015, Yu Li All rights reserved.

For research and education purpose only.