@article{Bian2020gms,
	title={{GMS}: Grid-based Motion Statistics for Fast, Ultra-Robust Feature Correspondence},
	author={Bian, JiaWang and Lin, Wen-Yan and Liu, Yun and Zhang, Le and Yeung, Sai-Kit and Cheng, Ming-Ming and Reid, Ian},
	journal={International Journal of Computer Vision (IJCV)},
	year={2020}
}

1.OpenCV 3.0 or later (for ORB features, necessary)
2.cudafeatures2d module(for gpu nearest neighbor, optional)
3.OpenCV xfeatures2D moudle (if using the opencv built-in GMS function) 

See src/demo.cpp

Go to "python" folder. Run "python3 opencv_demo.py". 
(You need install opencv_contrib by "pip install opencv-contrib-python")

1. Go to "matlab" folder. Compile the code with OpenCV ('Compile.m'), and run 'demo.m'.

In src/demo.cpp
	1.	#define USE_GPU" (need cudafeatures2d module) 
			using cpu mode by commenting it.
			
	2.	We suggest using SIFT features for accuracy, and using ORB features for speed.
In gms_matcher.h
			
	2.	#define THRESH_FACTOR 6		
			Set it higher for more input matches, and lower for the fewer input matches.
			Often 6 for ORB all matches, and 4 or 3 for SIFT matches (after ratio test).
			
	3. 	int GetInlierMask(vector<bool> &vbInliers, bool WithScale = false, bool WithRotation = false)
			Set WithScale to be true for wide-baseline matching and false for video matching.
			Set WithRotation to be true if images have significant reative rotations.