Abstract
Surface matching is a fundamental task in 3D computer vision, typically tackled by describing and matching local features computed from the 3D surface. As a result, description of local features lays the foundations for a variety of applications processing 3D data, such as 3D object recognition, 3D registration and reconstruction, and SLAM. A variety of algorithms for 3D feature description exists in the scientific literature. The majority of them are based on different, handcrafted ways to encode and exploit the geometric properties of a given surface. Recently, the success of deep neural networks for processing images has fueled also a data-driven approach to learn descriptive features from 3D data. This chapter provides a comprehensive review of the main proposals in the field.
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