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GMS: Grid-based Motion Statistics for Fast, Ultra-robust Feature Correspondence

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JiaWang Bian, Wen-Yan Lin, Yasuyuki Matsushita, Sai-Kit Yeung, Tan Dat Nguyen, Ming-Ming Cheng

GMS: Grid-based Motion Statistics for Fast, Ultra-robust Feature Correspondence IEEE CVPR, 2017

[Project Page] [pdf] [Bib] [Code] [Youtube]

Other Resouces

The method has been integrated into OpenCV library (see xfeatures2d in opencv_contrib).

The paper was selected and reviewed by Computer Vision News.

More experiments are shown in MatchBench.



1.OpenCV 3.0 or later (for IO and ORB features, necessary)

2.cudafeatures2d module(for gpu nearest neighbor, optional)

C++ Example:

Image pair demo in demo.cpp.

Matlab Example

You should compile the code with opencv library firstly(see the 'Compile.m').

Python Example:

Use Python3 to run gms_matcher script.

Tune Parameters:

In demo.cpp
	1.	#define USE_GPU" (need cudafeatures2d module) 
			using cpu mode by commenting it.
	2.	For high-resolution images, we suggest using 100K features with setFastThreshod(5);
	3.	For low-resolution (like VGA) images, we suggest using 10K features with setFastThreshod(0);

In gms_matcher.h
	2.	#define THRESH_FACTOR 6			
			The higher, the less matches。
	3. 	int GetInlierMask(vector<bool> &vbInliers, bool WithScale = false, bool WithRotation = false)
			Set WithScale to be true for unordered image matching and false for video matching.

If you like this work, please cite our paper

  title={GMS: Grid-based Motion Statistics for Fast, Ultra-robust Feature Correspondence},
  author={JiaWang Bian and Wen-Yan Lin and Yasuyuki Matsushita and Sai-Kit Yeung and Tan Dat Nguyen and Ming-Ming Cheng},
  booktitle={IEEE Conference on Computer Vision and Pattern Recognition},