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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA)
Proença, P., Gaspar, F. & Dias, J. (2015). SHREC’15 Track: Retrieval of Oobjects captured with kinect one camera. In I. Pratikakis, M. Spagnuolo, T. Theoharis, L. Van Gool, and R. Veltkamp (Ed.), Eurographics Workshop on 3D Object Retrieval (2015). (pp. 145-151). Suiça: Eurographics Association.
Exportar Referência (IEEE)
P. F. Proenca et al.,  "SHREC’15 Track: Retrieval of Oobjects captured with kinect one camera", in Eurographics Workshop on 3D Object Retrieval (2015), I. Pratikakis, M. Spagnuolo, T. Theoharis, L. Van Gool, and R. Veltkamp, Ed., Suiça, Eurographics Association, 2015, pp. 145-151
Exportar BibTeX
@inproceedings{proenca2015_1708549643851,
	author = "Proença, P. and Gaspar, F. and Dias, J.",
	title = "SHREC’15 Track: Retrieval of Oobjects captured with kinect one camera",
	booktitle = "Eurographics Workshop on 3D Object Retrieval (2015)",
	year = "2015",
	editor = "I. Pratikakis, M. Spagnuolo, T. Theoharis, L. Van Gool, and R. Veltkamp",
	volume = "",
	number = "",
	series = "",
	doi = "10.2312/3dor.20151068",
	pages = "145-151",
	publisher = "Eurographics Association",
	address = "Suiça",
	organization = "Eurographics Association",
	url = "https://diglib.eg.org/handle/10.2312/12281"
}
Exportar RIS
TY  - CPAPER
TI  - SHREC’15 Track: Retrieval of Oobjects captured with kinect one camera
T2  - Eurographics Workshop on 3D Object Retrieval (2015)
AU  - Proença, P.
AU  - Gaspar, F.
AU  - Dias, J.
PY  - 2015
SP  - 145-151
DO  - 10.2312/3dor.20151068
CY  - Suiça
UR  - https://diglib.eg.org/handle/10.2312/12281
AB  - Low-cost RGB-D sensing technology, such as the Microsoft Kinect, is gaining acceptance in the scientific community and even entering into our homes. This technology enables ordinary users to capture everyday object into digital 3D representations. Considering the image retrieval context, whereas the ability to digitalize photos led to a rapid increase of large collections of images, which in turn raised the need of efficient search and retrieval techniques. We believe the same is happening now for the 3D domain. Therefore, it is essential to identify which 3D shape descriptors, provide better matching and retrieval of such digitalized objects. In this paper, we start by presenting a collection of 3D objects acquired using the latest version of Microsoft Kinect, namely, Kinect One.
This dataset, comprising 175 common household objects classified into 18 different classes, was then used for the SHape REtrieval Contest (SHREC). Two groups have submitted their 3D matching techniques, providing the rank list with top 10 results, using the complete set of 175 objects as queries.
ER  -