SHOT: Unique signatures of histograms for surface and texture description

•This paper presents a local 3D descriptor for surface matching dubbed SHOT.•Our proposal includes a repeatable local reference frame as well as a 3D descriptor.•It enables seamless description of shape and color data from RGB-D sensors.•It is validated in object recognition, 3D reconstruction and s...

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Vydané v:	Computer vision and image understanding Ročník 125; s. 251 - 264
Hlavní autori:	Salti, Samuele, Tombari, Federico, Di Stefano, Luigi
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Amsterdam Elsevier Inc 01.08.2014 Elsevier
Predmet:	3D descriptors 3D reconstruction Applied sciences Artificial intelligence Computer science; control theory; systems Exact sciences and technology Histograms Object recognition Pattern recognition. Digital image processing. Computational geometry Proposals Robustness Sensors Shot Signatures Surface matching Texture Three dimensional 3D descriptors 3D reconstruction Object recognition Surface matching Availability Computer vision Histogram Information integration Stimulus Image processing Taxonomy Pattern recognition Texture Color image Depth of field Image reconstruction Computational geometry Stimulus complexity Tridimensional image Robustness Surface conditions Frame of reference
ISSN:	1077-3142, 1090-235X
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Abstract	•This paper presents a local 3D descriptor for surface matching dubbed SHOT.•Our proposal includes a repeatable local reference frame as well as a 3D descriptor.•It enables seamless description of shape and color data from RGB-D sensors.•It is validated in object recognition, 3D reconstruction and shape retrieval scenarios.•SHOT offers superior effectiveness with remarkably good computational efficiency. 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.
AbstractList	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. •This paper presents a local 3D descriptor for surface matching dubbed SHOT.•Our proposal includes a repeatable local reference frame as well as a 3D descriptor.•It enables seamless description of shape and color data from RGB-D sensors.•It is validated in object recognition, 3D reconstruction and shape retrieval scenarios.•SHOT offers superior effectiveness with remarkably good computational efficiency. 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.
Author	Tombari, Federico Di Stefano, Luigi Salti, Samuele
Author_xml	– sequence: 1 givenname: Samuele surname: Salti fullname: Salti, Samuele email: samuele.salti@unibo.it – sequence: 2 givenname: Federico surname: Tombari fullname: Tombari, Federico – sequence: 3 givenname: Luigi surname: Di Stefano fullname: Di Stefano, Luigi
BackLink	http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=28614677$$DView record in Pascal Francis
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CODEN	CVIUF4
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Keywords	3D descriptors 3D reconstruction Object recognition Surface matching Availability Computer vision Histogram Information integration Stimulus Image processing Taxonomy Pattern recognition Texture Color image Depth of field Image reconstruction Computational geometry Stimulus complexity Tridimensional image Robustness Surface conditions Frame of reference
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Snippet	•This paper presents a local 3D descriptor for surface matching dubbed SHOT.•Our proposal includes a repeatable local reference frame as well as a 3D... 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...
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SubjectTerms	3D descriptors 3D reconstruction Applied sciences Artificial intelligence Computer science; control theory; systems Exact sciences and technology Histograms Object recognition Pattern recognition. Digital image processing. Computational geometry Proposals Robustness Sensors Shot Signatures Surface matching Texture Three dimensional
Title	SHOT: Unique signatures of histograms for surface and texture description
URI	https://dx.doi.org/10.1016/j.cviu.2014.04.011 https://www.proquest.com/docview/1551108951
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