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Graph."],"published-print":{"date-parts":[[2019,2,28]]},"abstract":"<jats:p>\n            The process of aligning a pair of shapes is a fundamental operation in computer graphics. Traditional approaches rely heavily on matching corresponding points or features to guide the alignment, a paradigm that falters when significant shape portions are missing. These techniques generally do not incorporate prior knowledge about expected shape characteristics, which can help compensate for any misleading cues left by inaccuracies exhibited in the input shapes. We present an approach based on a deep neural network, leveraging shape datasets to learn a\n            <jats:italic>shape-aware<\/jats:italic>\n            prior for source-to-target alignment that is robust to shape incompleteness. In the absence of ground truth alignments for supervision, we train a network on the task of shape alignment using incomplete shapes generated from full shapes for self-supervision. Our network, called\n            <jats:italic>ALIGNet<\/jats:italic>\n            , is trained to warp complete source shapes to incomplete targets, as if the target shapes were complete, thus essentially rendering the alignment\n            <jats:italic>partial-shape agnostic<\/jats:italic>\n            . We aim for the network to develop specialized expertise over the common characteristics of the shapes in each dataset, thereby achieving a higher-level understanding of the expected shape space to which a local approach would be oblivious. We constrain\n            <jats:italic>ALIGNet<\/jats:italic>\n            through an anisotropic total variation identity regularization to promote piecewise smooth deformation fields, facilitating both partial-shape agnosticism and post-deformation applications. We demonstrate that\n            <jats:italic>ALIGNet<\/jats:italic>\n            learns to align geometrically distinct shapes and is able to infer plausible mappings even when the target shape is significantly incomplete. We show that our network learns the common expected characteristics of shape collections without over-fitting or memorization, enabling it to produce plausible deformations on unseen data during test time.\n          <\/jats:p>","DOI":"10.1145\/3267347","type":"journal-article","created":{"date-parts":[[2018,12,14]],"date-time":"2018-12-14T13:19:17Z","timestamp":1544793557000},"page":"1-14","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":43,"title":["ALIGNet"],"prefix":"10.1145","volume":"38","author":[{"given":"Rana","family":"Hanocka","sequence":"first","affiliation":[{"name":"Tel Aviv University, Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Noa","family":"Fish","sequence":"additional","affiliation":[{"name":"Tel Aviv University, Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenhua","family":"Wang","sequence":"additional","affiliation":[{"name":"Hebrew University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Raja","family":"Giryes","sequence":"additional","affiliation":[{"name":"Tel Aviv University, Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shachar","family":"Fleishman","sequence":"additional","affiliation":[{"name":"Intel Corporation"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Cohen-Or","sequence":"additional","affiliation":[{"name":"Tel Aviv University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2018,12,14]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/1399504.1360684"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/34.993558"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/34.121791"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/34.24792"},{"key":"e_1_2_1_6_1","unstructured":"Christopher B. 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Recognizing objects in adversarial clutter: Breaking a visual CAPTCHA. In Proceedings of the 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 1. I--134--I--141 vol.1."},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2010.46"},{"key":"e_1_2_1_22_1","volume-title":"Proceedings of the 3rd International Conference on 3-D Digital Imaging and Modeling. 145--152","author":"Rusinkiewicz S.","unstructured":"S. Rusinkiewicz and M. Levoy . 2001. Efficient variants of the ICP algorithm . In Proceedings of the 3rd International Conference on 3-D Digital Imaging and Modeling. 145--152 . S. Rusinkiewicz and M. Levoy. 2001. Efficient variants of the ICP algorithm. 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