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Graph."],"published-print":{"date-parts":[[2018,8,31]]},"abstract":"<jats:p>\n            We introduce P2P-NET, a\n            <jats:italic>general-purpose<\/jats:italic>\n            deep neural network which learns geometric transformations between point-based shape representations from two domains, e.g., meso-skeletons and surfaces, partial and complete scans, etc. The architecture of the P2P-NET is that of a\n            <jats:italic>bi-directional point displacement<\/jats:italic>\n            network, which transforms a source point set to a prediction of the target point set with the same cardinality, and vice versa, by applying\n            <jats:italic>point-wise<\/jats:italic>\n            displacement vectors learned from data. P2P-NET is trained on\n            <jats:italic>paired<\/jats:italic>\n            shapes from the source and target domains, but\n            <jats:italic>without<\/jats:italic>\n            relying on point-to-point correspondences between the source and target point sets. The training loss combines two uni-directional geometric losses, each enforcing a\n            <jats:italic>shape-wise<\/jats:italic>\n            similarity between the predicted and the target point sets, and a cross-regularization term to encourage consistency between displacement vectors going in opposite directions. We develop and present several different applications enabled by our general-purpose bidirectional P2P-NET to highlight the effectiveness, versatility, and potential of our network in solving a variety of point-based shape transformation problems.\n          <\/jats:p>","DOI":"10.1145\/3197517.3201288","type":"journal-article","created":{"date-parts":[[2018,7,31]],"date-time":"2018-07-31T15:56:23Z","timestamp":1533052583000},"page":"1-13","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":90,"title":["P2P-NET"],"prefix":"10.1145","volume":"37","author":[{"given":"Kangxue","family":"Yin","sequence":"first","affiliation":[{"name":"Simon Fraser University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Huang","sequence":"additional","affiliation":[{"name":"Shenzhen University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Cohen-Or","sequence":"additional","affiliation":[{"name":"Shenzhen University and Tel Aviv University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Simon Fraser University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2018,7,30]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2003.1175093"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.12802"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.13132"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2014.6907311"},{"key":"e_1_2_1_5_1","volume-title":"Proc. of ICRA.","author":"Byravan Arunkumar","year":"2016","unstructured":"Arunkumar Byravan and Dieter Fox . 2016 . 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