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Graph."],"published-print":{"date-parts":[[2016,7,11]]},"abstract":"<jats:p>\n            We introduce a co-analysis method which learns a\n            <jats:italic>functionality model<\/jats:italic>\n            for an object category, e.g., strollers or backpacks. Like previous works on functionality, we analyze object-to-object interactions and intra-object properties and relations. Differently from previous works, our model goes beyond providing a functionality-oriented descriptor for a single object; it prototypes the functionality of a category of 3D objects by co-analyzing typical interactions involving objects from the category. Furthermore, our co-analysis localizes the studied properties to the specific locations, or surface patches, that support specific functionalities, and then integrates the patch-level properties into a\n            <jats:italic>category<\/jats:italic>\n            functionality model. Thus our model focuses on the\n            <jats:italic>how<\/jats:italic>\n            , via common interactions, and\n            <jats:italic>where<\/jats:italic>\n            , via patch localization, of functionality analysis.\n          <\/jats:p>\n          <jats:p>\n            Given a collection of 3D objects belonging to the same category, with each object provided within a scene context, our co-analysis yields a set of\n            <jats:italic>proto-patches<\/jats:italic>\n            , each of which is a patch prototype supporting a specific type of interaction, e.g., stroller handle held by hand. The learned category functionality model is composed of proto-patches, along with their pairwise relations, which together summarize the functional properties of all the patches that appear in the input object category. With the learned functionality models for various object categories serving as a knowledge base, we are able to form a functional understanding of an individual 3D object, without a scene context. With patch localization in the model, functionality-aware\n            <jats:italic>modeling<\/jats:italic>\n            , e.g, functional object enhancement and the creation of functional object hybrids, is made possible.\n          <\/jats:p>","DOI":"10.1145\/2897824.2925870","type":"journal-article","created":{"date-parts":[[2016,7,11]],"date-time":"2016-07-11T16:04:33Z","timestamp":1468253073000},"page":"1-13","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":45,"title":["Learning how objects function via co-analysis of interactions"],"prefix":"10.1145","volume":"35","author":[{"given":"Ruizhen","family":"Hu","sequence":"first","affiliation":[{"name":"Shenzhen University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Oliver","family":"van Kaick","sequence":"additional","affiliation":[{"name":"Carleton University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bojian","family":"Wu","sequence":"additional","affiliation":[{"name":"SIAT"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hui","family":"Huang","sequence":"additional","affiliation":[{"name":"Shenzhen University and SIAT"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ariel","family":"Shamir","sequence":"additional","affiliation":[{"name":"The Interdisciplinary Center"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Simon Fraser University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2016,7,11]]},"reference":[{"key":"e_1_2_2_1_1","first-page":"1","article-title":"Functional 3D object classification using simulation of embodied agent","volume":"32","author":"Bar-Aviv E.","year":"2006","journal-title":"British Machine Vision Conference"},{"key":"e_1_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"e_1_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1111\/1467-8659.00669"},{"key":"e_1_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/2601097.2601185"},{"key":"e_1_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/2366145.2366154"},{"key":"e_1_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/2816795.2818057"},{"key":"e_1_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2011.5995327"},{"key":"e_1_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1037\/xge0000129"},{"key":"e_1_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/2766914"},{"key":"e_1_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/2601097.2601117"},{"key":"e_1_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/2516971.2516975"},{"key":"e_1_2_2_12_1","doi-asserted-by":"crossref","unstructured":"Mitra N. Wand M. Zhang H. Cohen-Or D. and Bokeloh M. 2013. Structure-aware shape processing. In Eurographics State-of-the-art Report (STAR).  Mitra N. Wand M. Zhang H. Cohen-Or D. and Bokeloh M. 2013. Structure-aware shape processing. In Eurographics State-of-the-art Report (STAR) .","DOI":"10.1145\/2542266.2542267"},{"key":"e_1_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2012.07.064"},{"key":"e_1_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/2661229.2661230"},{"key":"e_1_2_2_15_1","volume-title":"Proc. Int. Conf. AI and Stat., 456--463","author":"Schmidt M."},{"key":"e_1_2_2_16_1","unstructured":"Schultz M. and Joachims T. 2004. Learning a distance metric from relative comparisons. Advances in neural information processing systems (NIPS) 41.  Schultz M. and Joachims T. 2004. Learning a distance metric from relative comparisons. 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