keypoint-algorithms-benchmark

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Detector-descriptor benchmark

This is a benchmark for evaluation of state-of-the-art detector and descriptor algorithms, a source code for the "On the Comparison of Classic and Deep Keypoint Detector and Descriptor Methods" paper published on ISPA conference: https://arxiv.org/abs/2007.10000 [1].

HPSequences dataset

Download dataset: HPSequences dataset [1.3GB]

Place the directory in a convenient location. The folder hpatches-sequences-release contains all the 116 directories, 57 of which represent only photometric changes, whereas 59 represent only geometric deformations. Each sequence consists of one reference image and 5 target images representing the appropriate illumination or viewpoint changes. Alongisde every target image there is a homography connecting it to the reference image (stored in files H_1_<seq_num>). In case of an illumination change sequence, the homography is an identity mapping.

The sequence folders are named with the following convention:

• i_X: image sequences with illumination changes
• v_X: image sequences with viewpoint changes

Results

Remarks

This benchmark is based on the HPatches evaluation tasks [2] and HPSequences dataset published along with it (HPatches dataset repository). Thanks to the authors for providing the dataset and the evaluation details.

References

[1] On the Comparison of Classic and Deep Keypoint Detector and Descriptor Methods, Kristijan Bartol*, David Bojanić*, Tomislav Pribanić, Tomislav Petković, Yago Diez Donoso, Joaquim Salvi Mas, ISPA 2019. *Authors contributed equally.

[2] HPatches: A benchmark and evaluation of handcrafted and learned local descriptors, Vassileios Balntas*, Karel Lenc*, Andrea Vedaldi and Krystian Mikolajczyk, CVPR 2017. *Authors contributed equally.