https://medium.com/analytics-vidhya/introduction-to-orb-oriented-fast-and-rotated-brief-4220e8ec40cf
https://medium.com/@vad710/cv-for-busy-developers-describing-features-49530f372fbb
https://www.reddit.com/r/computervision/comments/fednny/sift_patent_expires_today/
https://www.pyimagesearch.com/2015/04/13/implementing-rootsift-in-python-and-opencv/
https://www.robots.ox.ac.uk/~vgg/publications/2012/Arandjelovic12/presentation.pdf
https://www.visuallocalization.net/benchmark/
http://www.pointclouds.org/assets/uploads/cglibs13_features.pdf
ORB or BRIEF descriptors https://github.com/dorian3d/DBoW2
A sample ORB vocabulary file can be downloaded from here https://drive.google.com/open?id=1wUPb328th8bUqhOk-i8xllt5mgRW4n84
https://github.com/xdspacelab/openvslam
A comparative analysis of SIFT, SURF, KAZE, AKAZE, ORB, and BRISK
Akaze_liop: Wang, Z., Fan, B., Wu, F.: Local intensity order pattern for feature description. In: IEEE International Conference on Computer Vision (ICCV). pp.603–610 (2011)
Feature Evaluation with High-Resolution Images
general overview
http://www.willowgarage.com/sites/default/files/orb_final.pdf
https://medium.com/analytics-vidhya/introduction-to-orb-oriented-fast-and-rotated-brief-4220e8ec40cf
ORB: an efficient alternative to SIFT or SURF (with/without cuda)
https://www.researchgate.net/publication/221111151_ORB_an_efficient_alternative_to_SIFT_or_SURF
https://medium.com/analytics-vidhya/introduction-to-harris-corner-detector-32a88850b3f6
https://docs.opencv.org/3.4/dc/d0d/tutorial_py_features_harris.html
https://in.udacity.com/course/computer-vision-nanodegree--nd891
http://aishack.in/tutorials/harris-corner-detector/
https://in.udacity.com/course/computer-vision-nanodegree--nd891
http://aishack.in/tutorials/sift-scale-invariant-feature-transform-introduction/
https://medium.com/analytics-vidhya/introduction-to-surf-speeded-up-robust-features-c7396d6e7c4e
https://docs.opencv.org/3.0-beta/doc/py_tutorials/py_feature2d/py_surf_intro/py_surf_intro.html
https://in.udacity.com/course/computer-vision-nanodegree--nd891
https://www.vision.ee.ethz.ch/~surf/eccv06.pdf
Viswanathan, Deepak. “Features from Accelerated Segment Test ( FAST ).” (2011).
Edward Rosten and Tom Drummond, “Machine learning for high speed corner detection” in 9th European Conference on Computer Vision, vol. 1, 2006, pp. 430–443.
Edward Rosten, Reid Porter, and Tom Drummond, “Faster and better: a machine learning approach to corner detection” in IEEE Trans. Pattern Analysis and Machine Intelligence, 2010, vol 32, pp. 105–119.
https://docs.opencv.org/3.0-beta/doc/py_tutorials/py_feature2d/py_fast/py_fast.html
https://in.udacity.com/course/computer-vision-nanodegree--nd891
Binary robust independentelementary features
https://docs.opencv.org/3.1.0/dc/d7d/tutorial_py_brief.html
https://in.udacity.com/course/computer-vision-nanodegree--nd891
https://gilscvblog.com/2013/09/19/a-tutorial-on-binary-descriptors-part-2-the-brief-descriptor/
Efficient Discriminative Projectionsfor Compact Binary Descriptors
https://www.labri.fr/perso/vlepetit/pubs/trzcinski_eccv12.pdf
Brisk: Binary robust invariant scalablekeypoints
https://www.researchgate.net/publication/221110715_BRISK_Binary_Robust_invariant_scalable_keypoints
FREAK: Fast retina keypoint
https://www.researchgate.net/publication/258848394_FREAK_Fast_retina_keypoint
Boosting Binary Keypoint Descriptors
https://www.researchgate.net/publication/261259229_Boosting_Binary_Keypoint_Descriptors
Learning Image Descriptors with Boosting
LDAHash: Improved matching with smallerdescriptors
https://wiki.epfl.ch/edicpublic/documents/Candidacy%20exam/LDAHash.pdf
LATCH: learned arrangements of three patch codes. In:Winter Conf. on Applications of Comput.
https://talhassner.github.io/home/publication/2016_WACV_2
Fastest implementation of the fully scale- and rotation-invariant LATCH 512-bit binary feature descriptor as described in the 2015 paper by Levi and Hassner
https://github.com/komrad36/CLATCH
https://talhassner.github.io/home/projects/LATCH/CLATCH_ECCV2016.pdf
License: ask author
PN-Net: Conjoined Triple Deep Network for Learning Local Image Descriptors
https://github.com/vbalnt/pnnet
https://arxiv.org/abs/1601.05030
License: ask author
https://github.com/vbalnt/tfeat
http://www.bmva.org/bmvc/2016/papers/paper119/paper119.pdf
License: MIT License
Descriptor Matching with Convolutional Neural Networks: a Comparison to SIFT
https://arxiv.org/abs/1405.5769
Matchnet: Unifying featureand metric learning for patch-based matching
https://github.com/hanxf/matchnet
License: BSD 2-Clause "Simplified" License
Dis-criminative learning of deep convolutional feature point descriptors
https://icwww.epfl.ch/~trulls/pdf/iccv-2015-deepdesc.pdf
https://github.com/etrulls/deepdesc-release
License: Attribution-NonCommercial-ShareAlike 4.0 International
Learning local feature descriptors usingconvex optimisation
https://www.robots.ox.ac.uk/~vedaldi/assets/pubs/simonyan14learning.pdf
Learning to Compare Image Patches via Convolutional Neural Networks
https://arxiv.org/abs/1504.03641
Code for "GIFT: Learning Transformation-Invariant Dense Visual Descriptors via Group CNNs" NeurIPS 2019
https://github.com/zju3dv/GIFT
Implementation of ECCV'18 paper - GeoDesc: Learning Local Descriptors by Integrating Geometry Constraints
https://github.com/lzx551402/geodesc
PyTorch pre-trained model for real-time interest point detection, description, and sparse tracking (https://arxiv.org/abs/1712.07629)
https://github.com/magicleap/SuperPointPretrainedNetwork
GL3D (Geometric Learning with 3D Reconstruction): a large-scale database created for 3D reconstruction and geometry-related learning problems
https://github.com/lzx551402/GL3D
Code release for "learning to find good correspondences" CVPR 2018