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		<doi>10.1109/SIBGRAPI.2016.017</doi>
		<citationkey>LimaTeic:2016:EfGlPo</citationkey>
		<title>An Efficient Global Point Cloud Descriptor for Object Recognition and Pose Estimation</title>
		<format>On-line</format>
		<year>2016</year>
		<numberoffiles>1</numberoffiles>
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		<author>Lima, Joćo Paulo Silva do Monte,</author>
		<author>Teichrieb, Veronica,</author>
		<affiliation>Universidade Federal Rural de Pernambuco</affiliation>
		<affiliation>Universidade Federal de Pernambuco</affiliation>
		<editor>Aliaga, Daniel G.,</editor>
		<editor>Davis, Larry S.,</editor>
		<editor>Farias, Ricardo C.,</editor>
		<editor>Fernandes, Leandro A. F.,</editor>
		<editor>Gibson, Stuart J.,</editor>
		<editor>Giraldi, Gilson A.,</editor>
		<editor>Gois, Joćo Paulo,</editor>
		<editor>Maciel, Anderson,</editor>
		<editor>Menotti, David,</editor>
		<editor>Miranda, Paulo A. V.,</editor>
		<editor>Musse, Soraia,</editor>
		<editor>Namikawa, Laercio,</editor>
		<editor>Pamplona, Mauricio,</editor>
		<editor>Papa, Joćo Paulo,</editor>
		<editor>Santos, Jefersson dos,</editor>
		<editor>Schwartz, William Robson,</editor>
		<editor>Thomaz, Carlos E.,</editor>
		<e-mailaddress>jpsml@cin.ufpe.br</e-mailaddress>
		<conferencename>Conference on Graphics, Patterns and Images, 29 (SIBGRAPI)</conferencename>
		<conferencelocation>Sćo José dos Campos, SP, Brazil</conferencelocation>
		<date>4-7 Oct. 2016</date>
		<publisher>IEEE Computer Society“s Conference Publishing Services</publisher>
		<publisheraddress>Los Alamitos</publisheraddress>
		<booktitle>Proceedings</booktitle>
		<tertiarytype>Full Paper</tertiarytype>
		<transferableflag>1</transferableflag>
		<versiontype>finaldraft</versiontype>
		<keywords>cloud descriptor, object recognition, pose estimation.</keywords>
		<abstract>This paper presents a global point cloud descriptor to be used for efficient object recognition and pose estimation. The proposed method is based on the estimation of a reference frame for the whole point cloud that represents an object instance, which is used for aligning it with the canonical coordinate system. After that, a descriptor is computed for the aligned point cloud based on how its 3D points are spatially distributed. Such descriptor is also extended with color distribution throughout the aligned point cloud. The global alignment transforms of matched point clouds are used for computing object pose. The proposed approach was evaluated with a publicly available dataset, showing that it outperforms major state of the art global descriptors regarding recognition rate and performance and that it allows precise pose estimation.</abstract>
		<language>en</language>
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		<usergroup>jpsml@cin.ufpe.br</usergroup>
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		<citingitemlist>sid.inpe.br/sibgrapi/2016/07.02.23.50 7</citingitemlist>
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