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<dc:title>3D point cloud registration based on a purpose-designed similarity measure</dc:title>
<dc:creator>Torre Ferrero, Carlos</dc:creator>
<dc:creator>Llata García, José Ramón</dc:creator>
<dc:creator>Alonso Rentería, Luciano</dc:creator>
<dc:creator>Robla Gómez, María Sandra</dc:creator>
<dc:creator>González Sarabia, Esther</dc:creator>
<dc:contributor>Universidad de Cantabria</dc:contributor>
<dc:subject>Laser scanner</dc:subject>
<dc:subject>3D point cloud</dc:subject>
<dc:subject>Descriptor</dc:subject>
<dc:subject>Similarity measure</dc:subject>
<dc:subject>Coarse alignment</dc:subject>
<dc:subject>3D registration</dc:subject>
<dc:description>This article introduces a novel approach for finding a rigid transformation that coarsely aligns two 3D point clouds. The algorithm performs an iterative comparison between 2D descriptors by using a purpose-designed similarity measure in order to find correspondences between two 3D point clouds sensed from different positions of a free-form object. The descriptors (named with the acronym CIRCON) represent an ordered set of radial contours that are extracted around an interest-point within the point cloud. The search for correspondences is done iteratively, following a cell distribution that allows the algorithm to converge toward a candidate point. Using a single correspondence an initial estimation of the Euclidean transformation is computed and later refined by means of a multiresolution approach. This coarse alignment algorithm can be used for 3D modeling and object manipulation tasks such as "Bin Picking" when free-form objects are partially occluded or present symmetries.</dc:description>
<dc:description>This study was carried out with the support of the Spanish CICYT project DPI2006-15313</dc:description>
<dc:date>2013-09-06T12:58:45Z</dc:date>
<dc:date>2013-09-06T12:58:45Z</dc:date>
<dc:date>2012</dc:date>
<dc:type>info:eu-repo/semantics/article</dc:type>
<dc:type>publishedVersion</dc:type>
<dc:identifier>1687-6180</dc:identifier>
<dc:identifier>DPI2006-15313</dc:identifier>
<dc:identifier>http://hdl.handle.net/10902/3147</dc:identifier>
<dc:identifier>10.1186/1687-6180-2012-57</dc:identifier>
<dc:language>eng</dc:language>
<dc:rights>Atribución 3.0 España</dc:rights>
<dc:rights>http://creativecommons.org/licenses/by/3.0/es/</dc:rights>
<dc:rights>openAccess</dc:rights>
<dc:publisher>Hindawi Publishing Corporation-</dc:publisher>
<dc:publisher>SpringerOpen</dc:publisher>
<dc:source>EURASIP Journal on Advances in Signal Processing, 2012, 57</dc:source>
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<dc:contributor>Universidad de Cantabria</dc:contributor>
<dc:creator>Torre Ferrero, Carlos</dc:creator>
<dc:creator>Llata García, José Ramón</dc:creator>
<dc:creator>Alonso Rentería, Luciano</dc:creator>
<dc:creator>Robla Gómez, María Sandra</dc:creator>
<dc:creator>González Sarabia, Esther</dc:creator>
<dc:date>2012</dc:date>
<dc:description lang="es_ES">This article introduces a novel approach for finding a rigid transformation that coarsely aligns two 3D point clouds. The algorithm performs an iterative comparison between 2D descriptors by using a purpose-designed similarity measure in order to find correspondences between two 3D point clouds sensed from different positions of a free-form object. The descriptors (named with the acronym CIRCON) represent an ordered set of radial contours that are extracted around an interest-point within the point cloud. The search for correspondences is done iteratively, following a cell distribution that allows the algorithm to converge toward a candidate point. Using a single correspondence an initial estimation of the Euclidean transformation is computed and later refined by means of a multiresolution approach. This coarse alignment algorithm can be used for 3D modeling and object manipulation tasks such as "Bin Picking" when free-form objects are partially occluded or present symmetries.</dc:description>
<dc:identifier>http://hdl.handle.net/10902/3147</dc:identifier>
<dc:language>eng</dc:language>
<dc:publisher>Hindawi Publishing Corporation-</dc:publisher>
<dc:publisher>SpringerOpen</dc:publisher>
<dc:source>EURASIP Journal on Advances in Signal Processing, 2012, 57</dc:source>
<dc:subject>Sin materia</dc:subject>
<dc:subject lang="es_ES">Laser scanner</dc:subject>
<dc:subject lang="es_ES">3D point cloud</dc:subject>
<dc:subject lang="es_ES">Descriptor</dc:subject>
<dc:subject lang="es_ES">Similarity measure</dc:subject>
<dc:subject lang="es_ES">Coarse alignment</dc:subject>
<dc:subject lang="es_ES">3D registration</dc:subject>
<dc:title lang="es_ES">3D point cloud registration based on a purpose-designed similarity measure</dc:title>
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