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Graph."],"published-print":{"date-parts":[[2021,8,31]]},"abstract":"<jats:p>\n            Establishing a consistent normal orientation for point clouds is a notoriously difficult problem in geometry processing, requiring attention to both\n            <jats:italic>local<\/jats:italic>\n            and\n            <jats:italic>global<\/jats:italic>\n            shape characteristics. The normal direction of a point is a function of the\n            <jats:italic>local<\/jats:italic>\n            surface neighborhood; yet, point clouds do not disclose the full underlying surface structure. Even assuming known geodesic proximity, calculating a consistent normal orientation requires the global context. In this work, we introduce a novel approach for establishing a globally consistent normal orientation for point clouds. Our solution separates the\n            <jats:italic>local<\/jats:italic>\n            and\n            <jats:italic>global<\/jats:italic>\n            components into two different sub-problems. In the local phase, we train a neural network to learn a\n            <jats:italic>coherent<\/jats:italic>\n            normal direction per patch (\n            <jats:italic>i.e.<\/jats:italic>\n            , consistently oriented normals within a single patch). In the global phase, we propagate the orientation across all coherent patches using a dipole propagation. Our dipole propagation decides to orient each patch using the electric field defined by all previously orientated patches. This gives rise to a global propagation that is stable, as well as being robust to nearby surfaces, holes, sharp features and noise.\n          <\/jats:p>","DOI":"10.1145\/3450626.3459835","type":"journal-article","created":{"date-parts":[[2021,7,20]],"date-time":"2021-07-20T00:04:26Z","timestamp":1626739466000},"page":"1-14","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":63,"title":["Orienting point clouds with dipole propagation"],"prefix":"10.1145","volume":"40","author":[{"given":"Gal","family":"Metzer","sequence":"first","affiliation":[{"name":"Tel Aviv University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rana","family":"Hanocka","sequence":"additional","affiliation":[{"name":"Tel Aviv University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Denis","family":"Zorin","sequence":"additional","affiliation":[{"name":"New York University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Raja","family":"Giryes","sequence":"additional","affiliation":[{"name":"Tel Aviv University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniele","family":"Panozzo","sequence":"additional","affiliation":[{"name":"New York University"}],"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":[[2021,7,19]]},"reference":[{"key":"e_1_2_2_1_1","volume-title":"International conference on machine learning. 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