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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Shrnutí:•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.
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content type line 23
ISSN:1077-3142
1090-235X
DOI:10.1016/j.cviu.2014.04.011