This paper presents a local 3D descriptor for surface matching dubbed SHOT. Our proposal stems from a taxonomy of existing methods which highlights two major approaches, referred to as Signatures and Histograms, inherently emphasizing descriptiveness and robustness respectively. We formulate a comprehensive proposal which encompasses a repeatable local reference frame as well as a 3D descriptor, the latter featuring an hybrid structure between Signatures and Histograms so as to aim at a more favorable balance between descriptive power and robustness. A quite peculiar trait of our method concerns seamless integration of multiple cues within the descriptor to improve distinctiveness, which is particularly relevant nowadays due to the increasing availability of affordable RGB-D sensors which can gather both depth and color information. A thorough experimental evaluation based on datasets acquired with different types of sensors, including a novel RGB-D dataset, vouches that SHOT outperforms state-of-the-art local descriptors in experiments addressing descriptor matching for object recognition, 3D reconstruction and shape retrieval. 
SHOT: Unique signatures of histograms for surface and texture description