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Graph."],"published-print":{"date-parts":[[2015,11,4]]},"abstract":"<jats:p>\n            Detailed scanning of indoor scenes is tedious for humans. We propose autonomous scene scanning by a robot to relieve humans from such a laborious task. In an autonomous setting, detailed scene acquisition is inevitably\n            <jats:italic>coupled<\/jats:italic>\n            with scene analysis at the required level of detail. We develop a framework for object-level scene reconstruction coupled with object-centric scene analysis. As a result, the autoscanning and reconstruction will be\n            <jats:italic>object-aware<\/jats:italic>\n            , guided by the object analysis. The analysis is, in turn, gradually improved with progressively increased object-wise data fidelity. In realizing such a framework, we drive the robot to execute an iterative\n            <jats:italic>analyze-and-validate<\/jats:italic>\n            algorithm which interleaves between object analysis and guided validations.\n          <\/jats:p>\n          <jats:p>\n            The object analysis incorporates online learning into a robust graph-cut based segmentation framework, achieving a global update of object-level segmentation based on the knowledge gained from robot-operated local validation. Based on the current analysis, the robot performs\n            <jats:italic>proactive<\/jats:italic>\n            validation over the scene with physical push and scan refinement, aiming at reducing the uncertainty of both object-level segmentation and object-wise reconstruction. We propose a joint entropy to measure such uncertainty based on segmentation confidence and reconstruction quality, and formulate the selection of validation actions as a maximum information gain problem. The output of our system is a reconstructed scene with both object extraction and object-wise geometry fidelity.\n          <\/jats:p>","DOI":"10.1145\/2816795.2818075","type":"journal-article","created":{"date-parts":[[2015,10,27]],"date-time":"2015-10-27T12:36:39Z","timestamp":1445949399000},"page":"1-14","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":59,"title":["Autoscanning for coupled scene reconstruction and proactive object analysis"],"prefix":"10.1145","volume":"34","author":[{"given":"Kai","family":"Xu","sequence":"first","affiliation":[{"name":"Shenzhen VisuCA Key Lab \/ SIAT and National University of Defense Technology"}]},{"given":"Hui","family":"Huang","sequence":"additional","affiliation":[{"name":"Shenzhen VisuCA Key Lab \/ SIAT"}]},{"given":"Yifei","family":"Shi","sequence":"additional","affiliation":[{"name":"HPCL, National University of Defense Technology"}]},{"given":"Hao","family":"Li","sequence":"additional","affiliation":[{"name":"Shandong University"}]},{"given":"Pinxin","family":"Long","sequence":"additional","affiliation":[{"name":"Shenzhen VisuCA Key Lab \/ SIAT"}]},{"given":"Jianong","family":"Caichen","sequence":"additional","affiliation":[{"name":"National University of Defense Technology and Shenzhen VisuCA Key Lab \/ SIAT"}]},{"given":"Wei","family":"Sun","sequence":"additional","affiliation":[{"name":"Shenzhen VisuCA Key Lab \/ SIAT"}]},{"given":"Baoquan","family":"Chen","sequence":"additional","affiliation":[{"name":"Shandong University"}]}],"member":"320","published-online":{"date-parts":[[2015,11,2]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1177\/027836498800700603"},{"key":"e_1_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/1015330.1015424"},{"key":"e_1_2_2_3_1","unstructured":"Berger M. Tagliasacchi A. Seversky L. M. Alliez P. Levine J. A. Sharf A. and Silva C. 2014. State of the art in surface reconstruction from point clouds. Eurographics STAR 165--185.  Berger M. Tagliasacchi A. Seversky L. M. Alliez P. Levine J. A. Sharf A. and Silva C. 2014. State of the art in surface reconstruction from point clouds. Eurographics STAR 165--185."},{"key":"e_1_2_2_4_1","volume-title":"RSS Workshop on Robots in Clutter: Manipulation, Perception and Navigation in Human Environments.","author":"Bersch C.","unstructured":"Bersch , C. , Pangercic , D. , Osentoski , S. , Hausman , K. , Marton , Z.-C. , Ueda , R. , Okada , K. , and Beetz , M . 2012. Segmentation of cluttered scenes through interactive perception . In RSS Workshop on Robots in Clutter: Manipulation, Perception and Navigation in Human Environments. Bersch, C., Pangercic, D., Osentoski, S., Hausman, K., Marton, Z.-C., Ueda, R., Okada, K., and Beetz, M. 2012. Segmentation of cluttered scenes through interactive perception. 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