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.

Spezialetti, R., Salti, S., Di Stefano, L., Tombari, F. (2020). 3D Local Descriptors—from Handcrafted to Learned. Cham : Springer [10.1007/978-3-030-44070-1_7].

3D Local Descriptors—from Handcrafted to Learned

Spezialetti, Riccardo;Salti, Samuele;Di Stefano, Luigi;
2020

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.
2020
3D Imaging, Analysis and Applications
319
352
Spezialetti, R., Salti, S., Di Stefano, L., Tombari, F. (2020). 3D Local Descriptors—from Handcrafted to Learned. Cham : Springer [10.1007/978-3-030-44070-1_7].
Spezialetti, Riccardo; Salti, Samuele; Di Stefano, Luigi; Tombari, Federico
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/805330
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