Abstract
This paper presents an overview of the evolution of local features from
handcrafted to deep-learning-based methods, followed by a discussion of several
benchmarks and papers evaluating such local features. Our investigations are
motivated by 3D reconstruction problems, where the precise location of the
features is important. As we describe these methods, we highlight and explain
the challenges of feature extraction and potential ways to overcome them. We
first present handcrafted methods, followed by methods based on classical
machine learning and finally we discuss methods based on deep-learning. This
largely chronologically-ordered presentation will help the reader to fully
understand the topic of image and region description in order to make best use
of it in modern computer vision applications. In particular, understanding
handcrafted methods and their motivation can help to understand modern
approaches and how machine learning is used to improve the results. We also
provide references to most of the relevant literature and code.
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