opencv
diff --git a/‎modules/xfeatures2d/README.md
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diff --git a/‎modules/xfeatures2d/doc/xfeatures2d.bib
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diff --git a/‎modules/xfeatures2d/include/opencv2/xfeatures2d.hpp
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diff --git a/‎modules/xfeatures2d/perf/perf_beblid.cpp
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@@ -5,4 +5,4 @@ Extra 2D Features Framework
 2. Non-free 2D feature algorithms
 
 Extra 2D Features Framework containing experimental and non-free 2D feature detector/descriptor algorithms:
- SURF, BRIEF, Censure, Freak, LUCID, Daisy, Self-similar.
+ SURF, BRIEF, Censure, Freak, LUCID, Daisy, BEBLID, Self-similar.
@@ -140,4 +140,24 @@ @incollection{LUCID
   pages = {1--9}
   year = {2012}
   publisher = {NIPS}
+}
+
+@article{Suarez2020BEBLID,
+  title = {{BEBLID: Boosted Efficient Binary Local Image Descriptor}},
+  journal = {Pattern Recognition Letters},
+  volume = {133},
+  pages = {366--372},
+  year = {2020},
+  issn = {0167-8655},
+  doi = {https://doi.org/10.1016/j.patrec.2020.04.005},
+  url = {https://raw.githubusercontent.com/iago-suarez/BEBLID/master/BEBLID_Boosted_Efficient_Binary_Local_Image_Descriptor.pdf},
+  author = {Iago Su\'arez and Ghesn Sfeir and Jos\'e M. Buenaposada and Luis Baumela},
+}
+
+@inproceedings{winder2007learning,
+  title= {Learning Local Image Descriptors},
+  author= {Winder, Simon AJ and Brown, Matthew},
+  booktitle= {Computer Vision and Pattern Recognition},
+  pages={1--8},
+  year={2007},
 }
@@ -179,6 +179,51 @@ class CV_EXPORTS_W LATCH : public Feature2D
     CV_WRAP static Ptr<LATCH> create(int bytes = 32, bool rotationInvariance = true, int half_ssd_size = 3, double sigma = 2.0);
 };
 
+/** @brief Class implementing BEBLID (Boosted Efficient Binary Local Image Descriptor),
+ * described in @cite Suarez2020BEBLID .
+
+BEBLID \cite Suarez2020BEBLID is a efficient binary descriptor learned with boosting.
+It is able to describe keypoints from any detector just by changing the scale_factor parameter.
+In several benchmarks it has proved to largely improve other binary descriptors like ORB or
+BRISK with the same efficiency. BEBLID describes using the difference of mean gray values in
+different regions of the image around the KeyPoint, the descriptor is specifically optimized for
+image matching and patch retrieval addressing the asymmetries of these problems.
+
+If you find this code useful, please add a reference to the following paper:
+<BLOCKQUOTE> Iago Suárez, Ghesn Sfeir, José M. Buenaposada, and Luis Baumela.
+BEBLID: Boosted efficient binary local image descriptor.
+Pattern Recognition Letters, 133:366–372, 2020. </BLOCKQUOTE>
+
+The descriptor was trained using 1 million of randomly sampled pairs of patches
+(20% positives and 80% negatives) from the Liberty split of the UBC datasets
+\cite winder2007learning as described in the paper @cite Suarez2020BEBLID.
+You can check in the [AKAZE example](https://raw.githubusercontent.com/opencv/opencv/master/samples/cpp/tutorial_code/features2D/AKAZE_match.cpp)
+how well BEBLID works. Detecting 10000 keypoints with ORB and describing with BEBLID obtains
+561 inliers (75%) whereas describing with ORB obtains only 493 inliers (63%).
+*/
+class CV_EXPORTS_W BEBLID : public Feature2D
+{
+public:
+    /**
+     * @brief  Descriptor number of bits, each bit is a boosting weak-learner.
+     * The user can choose between 512 or 256 bits.
+     */
+    enum BeblidSize
+    {
+        SIZE_512_BITS = 100, SIZE_256_BITS = 101,
+    };
+    /** @brief Creates the BEBLID descriptor.
+    @param scale_factor Adjust the sampling window around detected keypoints:
+    - <b> 1.00f </b> should be the scale for ORB keypoints
+    - <b> 6.75f </b> should be the scale for SIFT detected keypoints
+    - <b> 6.25f </b> is default and fits for KAZE, SURF detected keypoints
+    - <b> 5.00f </b> should be the scale for AKAZE, MSD, AGAST, FAST, BRISK keypoints
+    @param n_bits Determine the number of bits in the descriptor. Should be either
+     BEBLID::SIZE_512_BITS or BEBLID::SIZE_256_BITS.
+    */
+    CV_WRAP static Ptr<BEBLID> create(float scale_factor, int n_bits = BEBLID::SIZE_512_BITS);
+};
+
 /** @brief Class implementing DAISY descriptor, described in @cite Tola10
 
 @param radius radius of the descriptor at the initial scale
 
@@ -0,0 +1,36 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at https://opencv.org/license.html.
+#include "perf_precomp.hpp"
+
+namespace opencv_test { namespace {
+
+typedef perf::TestBaseWithParam<std::string> beblid;
+
+#define BEBLID_IMAGES \
+    "cv/detectors_descriptors_evaluation/images_datasets/leuven/img1.png",\
+    "stitching/a3.png"
+
+#ifdef OPENCV_ENABLE_NONFREE
+PERF_TEST_P(beblid, extract, testing::Values(BEBLID_IMAGES))
+{
+    string filename = getDataPath(GetParam());
+    Mat frame = imread(filename, IMREAD_GRAYSCALE);
+    ASSERT_FALSE(frame.empty()) << "Unable to load source image " << filename;
+
+    Mat mask;
+    declare.in(frame).time(90);
+
+    Ptr<SURF> detector = SURF::create();
+    vector<KeyPoint> points;
+    detector->detect(frame, points, mask);
+
+    Ptr<BEBLID> descriptor = BEBLID::create(6.25f);
+    cv::Mat descriptors;
+    TEST_CYCLE() descriptor->compute(frame, points, descriptors);
+
+    SANITY_CHECK_NOTHING();
+}
+#endif // NONFREE
+
+}} // namespace