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Feature Matching

Applied Feature Extraction and Descriptor Method to robust matching !!

[Goal] We can apply various extracton and descriptor method for matching in challenging environment

[Advanced] Algorithm extended by applying the corresponding github page → Parallax

[Advantage] We can easily apply various method only changing parameters!!

[Without GPU Version] You can apply only traditional method (w/o GPU) version in other branch (called only_trad_method)

  • Challenging Environment
    Query Image Candidate Image

1. Dependencies

2. Methodology to apply feature matching

  • Feature Extraction Method

    • [1] ORB
    • [2] goodFeaturesToTrack
    • [3] SIFT
    • [4] SuperPoint

  • Feature Descriptor Method

  • Macher Method

    • [1] Brute-Force Matcher (BF)
    • [2] Fast Library for Approximate Nearest Neighbors Matcher (FLANN)

  • Matching Method

    • [1] vanilla match (VANILLA)
    • [2] knn match (KNN)
    • [3] radius match (RADIUS)

  • Outlier Rejection Method

    • [1] findFundamentalMatrix (FM)
    • [2] vector field consensus (VFC)

3. Build and Run

Clone the repository and build and run simultaneously:

   $ cd ${workspace}
   $ git clone https://github.com/SungJaeShin/Feature_matching.git
   $ cd Feature_matching
   $ sh start.sh

4. Changing Parameters

  • IMAGE_NUM (include.h): Put value to test image pair

  • EXTRACT_MODE (include.h):

    • 1 → apply ORB
    • 2 → apply GFTT (GoodFeaturesToTrack)
    • 3 → apply SIFT
    • 4 → apply GFTT(Query) + ORB(Candidate)
    • 5 → apply GFTT(Query) + SIFT(Candidate)
    • 6 → apply SuperPoint

  • DESCRIPTOR_MODE (include.h):

    • 1 → apply AKAZE
    • 2 → apply SURF
    • 3 → apply DAISY
    • 4 → apply SuperPoint

  • MATCHER_MODE (include.h):

    • 1 → apply BF Matcher (Brute-Force)
    • 2 → apply FLANN Matcher (Fast Library for Approximate Nearest Neighbors)

  • MATCHING_MODE (include.h):

    • 1 → apply vanilla match
    • 2 → apply knn match
    • 3 → apply radius match

  • REJECTION_MODE (include.h):

    • 1 → apply fundamentalMatrix
    • 2 → apply VFC

  • MIN_DIS (include.h): Put value to change feature's Euclidean distance when using GoodFeaturesToTrack extractor

  • superpoint_model_weight_path (matching.cpp): Put absolute path for getting pre-trained SuperPoint model

  • query_path, cand_path (matching.cpp): Put absolute path for getting query image and candidate image

  • save_path (plot.h): Put absolute path for saving matching result

5. Advanced Changing Parameters (Option)

This variables are not requirement, but the following variables are also considered to obtain more robust results.

  • [CASE 1] When you use BFMatcher, please change in create arguments

    • SIFT, SURF → cv::NORM_L1 & cv::NORM_L2
    • ORB → cv::NORM_HAMMING

  • [CASE 2] When you use radiusMatch, please change maxDistance argument

    • In above case, there are some preference radius results !! (Default = 0.3)
      • [Preference radius] 0.4 => 405 matches / 0.3 => 183 matches / 0.2 => 117 matches

  • [CASE 3] When you use findFundamentalMat, please change ransacReprojThreshold argument (Default = 1)

    • If you want to get more robust result, then change '0.1' or etc.

6. Results

  • Results of Inlier Number and Time Consumption

    • ORB Result

    • GFTT Result

    • SIFT Result

  • Matching Results Set

    ORB + AKAZE + FM (inliers 22) ORB + SURF + FM (inliers 44) ORB + DAISY + FM (inliers 57)
    GFTT + AKAZE + FM (inliers 10) GFTT + SURF + FM (inliers 9) GFTT + DAISY + FM (inliers 10)
    SIFT + AKAZE + FM (inliers 11) SIFT + SURF + FM (inliers 17) SIFT + DAISY + FM (inliers 19)
    GFTT + ORB + AKAZE + FM (inliers 10) GFTT + ORB + SURF + FM (inliers 10) GFTT + ORB + DAISY + FM (inliers 8)
    GFTT + SIFT + AKAZE + FM (inliers 10) GFTT + SIFT + SURF + FM (inliers 9) GFTT + SIFT + DAISY + FM (inliers 9)

  • SuperPoint Result (C++ Version)

    • Time consumption getting Pre-trained SuperPoint model: 21.1761 ms
    • Result of features using SuperPoint Model
      Query Features Candidate Features

  • SuperPoint + SuperGlue Result (Python Version) (Follow this Page !!)

    • Red: more confident, Blue: less confident
    Threshold = 0.3 (inliers 64) Threshold = 0.4 (inliers 52)
    Threshold = 0.5 (inliers 47) Threshold = 0.6 (inliers 40)
    Threshold = 0.7 (inliers 30) Threshold = 0.8 (inliers 25)

7. Best Results

  • C++ Results

    • ORB + SURF + BF + KNN + FM (ransacReprojThreshold = 1)

    • ORB + DAISY + BF + KNN + FM (ransacReprojThreshold = 1)

    • ORB + SURF + BF + KNN + FM (ransacReprojThreshold = 0.1)

    • ORB + DAISY + BF + KNN + FM (ransacReprojThreshold = 0.1)

    • ORB + SURF + BF + KNN + VFC

    • ORB + DAISY + BF + KNN + VFC

  • Python Version (Follow this Page !!)

    • SuperPoint + SuperGlue (matching threshold = 0.8) (Red: more confident, Blue: less confident)

8. Support Docker

Test on Ubuntu 18.04 equipped with P2000 NoteBook

9. Other Error Related

  • [1] SuperGlue Error
    • (1) ImportError: /home/sj/anaconda3/envs/feature_matching/bin/../lib/libgio-2.0.so.0: undefined symbol: g_unix_get_passwd_entry

10. Reference

[1] Jianbo Shi and Carlo Tomasi. Good features to track. In Computer Vision and Pattern Recognition, 1994. Proceedings CVPR'94., 1994 IEEE Computer Society Conference on, pages 593–600. IEEE, 1994.
[2] David G. Lowe. Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision, 60(2):91–110, November 2004.
[3] Herbert Bay, Tinne Tuytelaars, and Luc Van Gool. Surf: Speeded up robust features. Computer Vision–ECCV 2006, pages 404–417, 2006.
[4] Ethan Rublee, Vincent Rabaud, Kurt Konolige, Gary R. Bradski: ORB: An efficient alternative to SIFT or SURF. ICCV 2011: 2564-2571.
[5] Pablo Fernández Alcantarilla, Jesús Nuevo, and Adrien Bartoli. Fast explicit diffusion for accelerated features in nonlinear scale spaces. BMVC, pages 13.1–13.11, 2013.
[6] E. Tola, V. Lepetit, and P. Fua. Daisy: An efficient dense descriptor applied to wide baseline stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(5):815–830, May 2010.
[7] Jiayi Ma, Ji Zhao, Jinwen Tian, Alan Yuille, and Zhuowen Tu. Robust Point Matching via Vector Field Consensus, IEEE Transactions on Image Processing, 23(4), pp. 1706-1721, 2014
[8] Jiayi Ma, Ji Zhao, Jinwen Tian, Xiang Bai, and Zhuowen Tu. Regularized Vector Field Learning with Sparse Approximation for Mismatch Removal, Pattern Recognition, 46(12), pp. 3519-3532, 2013
[9] DeTone, Daniel & Malisiewicz, Tomasz & Rabinovich, Andrew. SuperPoint: Self-Supervised Interest Point Detection and Description. CVPR Deep Learning for Visual SLAM Workshop (CVPR), 2018
[10] https://bkshin.tistory.com/entry/OpenCV-28-%ED%8A%B9%EC%A7%95-%EB%A7%A4%EC%B9%ADFeature-Matching
[11] https://stackoverflow.com/questions/18744051/what-the-hessian-minimum-threshold-means-on-the-surffeaturedetector-function
[12] https://www.researchgate.net/figure/Relation-between-hessian-threshold-value-and-Accuracy-of-SURF-detector-As-shown-in_fig2_309363445
[13] https://docs.opencv.org/3.4/d9/d37/classcv_1_1xfeatures2d_1_1DAISY.html
[14] https://m.blog.naver.com/PostView.naver?isHttpsRedirect=true&blogId=pckbj123&logNo=100203116086
[15] https://076923.github.io/posts/Python-opencv-38/
[16] https://docs.opencv.org/3.4/db/d95/classcv_1_1ORB.html#adc371099dc902a9674bd98936e79739c
[17] https://bkshin.tistory.com/entry/OpenCV-27-%ED%8A%B9%EC%A7%95-%EB%94%94%EC%8A%A4%ED%81%AC%EB%A6%BD%ED%84%B0-%EA%B2%80%EC%B6%9C%EA%B8%B0-SIFT-SURF-ORB
[18] https://docs.opencv.org/3.4/d7/d60/classcv_1_1SIFT.html
[19] https://leechamin.tistory.com/330
[20] https://github.com/mpkuse/robust_feature_matching
[21] https://github.com/ChanWoo25/SuperPoint2CPP
[22] https://github.com/magicleap/SuperPointPretrainedNetwork
[23] https://github.com/magicleap/SuperGluePretrainedNetwork

11. Acknowledgement

Thanks for Yeeun Kim help to get robust feature matching method !!

12. Future Works

  • Apply GCBB (Linear time vehicle relocation in SLAM paper)