Unsupervised learning of 3D object models from partial views
We present an algorithm for learning 3D object models from partial object observations. The input to our algorithm is a sequence of 3D laser range scans. Models learned from the objects are represented as point clouds. Our approach can deal with partial views and it can robustly learn accurate model...
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| creator | Ruhnke, M. Steder, B. Grisetti, G. Burgard, W. |
| description | We present an algorithm for learning 3D object models from partial object observations. The input to our algorithm is a sequence of 3D laser range scans. Models learned from the objects are represented as point clouds. Our approach can deal with partial views and it can robustly learn accurate models from complex scenes. It is based on an iterative matching procedure which attempts to recursively merge similar models. The alignment between models is determined using a novel scan registration procedure based on range images. The decision about which models to merge is performed by spectral clustering of a similarity matrix whose entries represent the consistency between different models. |
| doi_str_mv | 10.1109/ROBOT.2009.5152524 |
| format | Conference Proceeding |
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The input to our algorithm is a sequence of 3D laser range scans. Models learned from the objects are represented as point clouds. Our approach can deal with partial views and it can robustly learn accurate models from complex scenes. It is based on an iterative matching procedure which attempts to recursively merge similar models. The alignment between models is determined using a novel scan registration procedure based on range images. The decision about which models to merge is performed by spectral clustering of a similarity matrix whose entries represent the consistency between different models.</description><subject>Clouds</subject><subject>Iterative algorithms</subject><subject>Laser modes</subject><subject>Layout</subject><subject>Merging</subject><subject>model learning</subject><subject>Object detection</subject><subject>range images</subject><subject>Robotics and automation</subject><subject>Robots</subject><subject>Robustness</subject><subject>Unsupervised learning</subject><issn>1050-4729</issn><issn>2577-087X</issn><isbn>1424427886</isbn><isbn>9781424427888</isbn><isbn>1424427894</isbn><isbn>9781424427895</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2009</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpFkMlKA0EURcshYBvzA7qpH-j4Xs0FbjSOEGiQBNyF6vQrqdBJh66Y4N8rGHB1Dxw4i8vYNcIYEfzte_VQzcYCwI81aqGFOmGXqIRSwjqvTlkhtLUlOPtx9i-cOWcFgoZSWeEHrPBQGgWo3QUb5bwCALRGSZQFu5tv8teW-n3K1PCWQr9Jm0_eRS4feVevaLnj666hNvPYd2u-Df0uhZbvEx3yFRvE0GYaHXfI5s9Ps8lrOa1e3ib30zKhVKpEj6Ai1bC0SphfaKzAunbR1040wqCRFFUTg3HgpHMxwtJrDaSdNk1AOWQ3f91ERIttn9ah_14cH5E_f2BN6Q</recordid><startdate>200905</startdate><enddate>200905</enddate><creator>Ruhnke, M.</creator><creator>Steder, B.</creator><creator>Grisetti, G.</creator><creator>Burgard, W.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>200905</creationdate><title>Unsupervised learning of 3D object models from partial views</title><author>Ruhnke, M. ; Steder, B. ; Grisetti, G. ; Burgard, W.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i1344-19104feb0c7426febd721bb8f9b82d26163ef4dfa6808388ff0c9550e5856da13</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Clouds</topic><topic>Iterative algorithms</topic><topic>Laser modes</topic><topic>Layout</topic><topic>Merging</topic><topic>model learning</topic><topic>Object detection</topic><topic>range images</topic><topic>Robotics and automation</topic><topic>Robots</topic><topic>Robustness</topic><topic>Unsupervised learning</topic><toplevel>online_resources</toplevel><creatorcontrib>Ruhnke, M.</creatorcontrib><creatorcontrib>Steder, B.</creatorcontrib><creatorcontrib>Grisetti, G.</creatorcontrib><creatorcontrib>Burgard, W.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Ruhnke, M.</au><au>Steder, B.</au><au>Grisetti, G.</au><au>Burgard, W.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Unsupervised learning of 3D object models from partial views</atitle><btitle>2009 IEEE International Conference on Robotics and Automation</btitle><stitle>ROBOT</stitle><date>2009-05</date><risdate>2009</risdate><spage>801</spage><epage>806</epage><pages>801-806</pages><issn>1050-4729</issn><eissn>2577-087X</eissn><isbn>1424427886</isbn><isbn>9781424427888</isbn><eisbn>1424427894</eisbn><eisbn>9781424427895</eisbn><abstract>We present an algorithm for learning 3D object models from partial object observations. 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| identifier | ISSN: 1050-4729 |
| ispartof | 2009 IEEE International Conference on Robotics and Automation, 2009, p.801-806 |
| issn | 1050-4729 2577-087X |
| language | eng |
| recordid | cdi_ieee_primary_5152524 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Clouds Iterative algorithms Laser modes Layout Merging model learning Object detection range images Robotics and automation Robots Robustness Unsupervised learning |
| title | Unsupervised learning of 3D object models from partial views |
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