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Graph."],"published-print":{"date-parts":[[2024,7,19]]},"abstract":"<jats:p>\n            Despite recent advances in reconstructing an organic model with the neural signed distance function (SDF), the high-fidelity reconstruction of a CAD model directly from low-quality unoriented point clouds remains a significant challenge. In this paper, we address this challenge based on the prior observation that the surface of a CAD model is generally composed of piecewise surface patches, each approximately developable even around the feature line. Our approach, named\n            <jats:italic>NeurCADRecon<\/jats:italic>\n            , is self-supervised, and its loss includes a developability term to encourage the Gaussian curvature toward 0 while ensuring fidelity to the input points (see the teaser figure). Noticing that the Gaussian curvature is non-zero at tip points, we introduce a double-trough curve to tolerate the existence of these tip points. Furthermore, we develop a dynamic sampling strategy to deal with situations where the given points are incomplete or too sparse. Since our resulting neural SDFs can clearly manifest sharp feature points\/lines, one can easily extract the feature-aligned triangle mesh from the SDF and then decompose it into smooth surface patches, greatly reducing the difficulty of recovering the parametric CAD design. A comprehensive comparison with existing state-of-the-art methods shows the significant advantage of our approach in reconstructing faithful CAD shapes.\n          <\/jats:p>","DOI":"10.1145\/3658171","type":"journal-article","created":{"date-parts":[[2024,7,19]],"date-time":"2024-07-19T14:47:57Z","timestamp":1721400477000},"page":"1-17","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":21,"title":["NeurCADRecon: Neural Representation for Reconstructing CAD Surfaces by Enforcing Zero Gaussian Curvature"],"prefix":"10.1145","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6271-2546","authenticated-orcid":false,"given":"Qiujie","family":"Dong","sequence":"first","affiliation":[{"name":"Shandong University, Qingdao, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8273-1808","authenticated-orcid":false,"given":"Rui","family":"Xu","sequence":"additional","affiliation":[{"name":"Shandong University, Qingdao, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0938-267X","authenticated-orcid":false,"given":"Pengfei","family":"Wang","sequence":"additional","affiliation":[{"name":"The University of Hong Kong, HongKong, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0835-3316","authenticated-orcid":false,"given":"Shuangmin","family":"Chen","sequence":"additional","affiliation":[{"name":"Qingdao University of Science and Technology, Qingdao, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8452-8723","authenticated-orcid":false,"given":"Shiqing","family":"Xin","sequence":"additional","affiliation":[{"name":"Shandong University, Qingdao, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6206-3216","authenticated-orcid":false,"given":"Xiaohong","family":"Jia","sequence":"additional","affiliation":[{"name":"AMSS, Chinese Academy of Sciences, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2284-3952","authenticated-orcid":false,"given":"Wenping","family":"Wang","sequence":"additional","affiliation":[{"name":"Texas A&amp;M University, Texas, United States of America"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1231-3392","authenticated-orcid":false,"given":"Changhe","family":"Tu","sequence":"additional","affiliation":[{"name":"Shandong University, Qingdao, China"}]}],"member":"320","published-online":{"date-parts":[[2024,7,19]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"SAL: Sign Agnostic Learning of Shapes From Raw Data. 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