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Graph."],"published-print":{"date-parts":[[2017,8,31]]},"abstract":"<jats:p>\n            Many approaches to shape comparison and recognition start by establishing a shape correspondence. We \"turn the table\" and show that quality shape correspondences can be obtained by performing many shape recognition tasks. What is more, the method we develop computes a\n            <jats:italic>fine-grained, topology-varying<\/jats:italic>\n            part correspondence between two 3D shapes where the core evaluation mechanism only recognizes shapes\n            <jats:italic>globally.<\/jats:italic>\n            This is made possible by casting the part correspondence problem in a deformation-driven framework and relying on a\n            <jats:italic>data-driven<\/jats:italic>\n            \"deformation energy\" which rates visual similarity between deformed shapes and models from a shape repository. Our basic premise is that if a correspondence between two chairs (or airplanes, bicycles, etc.) is correct, then a reasonable deformation between the two chairs anchored on the correspondence ought to produce\n            <jats:italic>plausible<\/jats:italic>\n            , \"chair-like\" in-between shapes.\n          <\/jats:p>\n          <jats:p>Given two 3D shapes belonging to the same category, we perform a top-down, hierarchical search for part correspondences. For a candidate correspondence at each level of the search hierarchy, we deform one input shape into the other, while respecting the correspondence, and rate the correspondence based on how well the resulting deformed shapes resemble other shapes from ShapeNet belonging to the same category as the inputs. The resemblance, i.e., plausibility, is measured by comparing multi-view depth images over category-specific features learned for the various shape categories. We demonstrate clear improvements over state-of-the-art approaches through tests covering extensive sets of man-made models with rich geometric and topological variations.<\/jats:p>","DOI":"10.1145\/3072959.3073613","type":"journal-article","created":{"date-parts":[[2017,7,21]],"date-time":"2017-07-21T12:24:07Z","timestamp":1500639847000},"page":"1-12","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":12,"title":["Deformation-driven shape correspondence via shape recognition"],"prefix":"10.1145","volume":"36","author":[{"given":"Chenyang","family":"Zhu","sequence":"first","affiliation":[{"name":"Simon Fraser University and National University of Defense Technology"}]},{"given":"Renjiao","family":"Yi","sequence":"additional","affiliation":[{"name":"Simon Fraser University and National University of Defense Technology"}]},{"given":"Wallace","family":"Lira","sequence":"additional","affiliation":[{"name":"Simon Fraser University"}]},{"given":"Ibraheem","family":"Alhashim","sequence":"additional","affiliation":[{"name":"Simon Fraser University"}]},{"given":"Kai","family":"Xu","sequence":"additional","affiliation":[{"name":"National University of Defense Technology"}]},{"given":"Hao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Simon Fraser University"}]}],"member":"320","published-online":{"date-parts":[[2017,7,20]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/2601097.2601102"},{"key":"e_1_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/2816795.2818088"},{"key":"e_1_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/882262.882311"},{"key":"e_1_2_2_4_1","volume-title":"Mid-level elements for object detection. arXiv preprint arXiv:1504.07284","author":"Bansal Aayush","year":"2015","unstructured":"Aayush Bansal , Abhinav Shrivastava , Carl Doersch , and Abhinav Gupta . 2015. 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Structure-aware Shape Processing. In SIGGRAPH Asia 2013 Courses. 1:1--1:20.  Niloy Mitra Michael Wand Hao (Richard) Zhang Daniel Cohen-Or Vladimir Kim and Qi-Xing Huang. 2013. Structure-aware Shape Processing. 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