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Graph."],"published-print":{"date-parts":[[2022,7]]},"abstract":"<jats:p>\n            We view the reconstruction of CAD models in the boundary representation (B-Rep) as the detection of geometric primitives of different orders,\n            <jats:italic>i.e.<\/jats:italic>\n            , vertices, edges and surface patches, and the correspondence of primitives, which are holistically modeled as a chain complex, and show that by modeling such comprehensive structures more complete and regularized reconstructions can be achieved. We solve the complex generation problem in two steps. First, we propose a novel neural framework that consists of a sparse CNN encoder for input point cloud processing and a tri-path transformer decoder for generating geometric primitives and their mutual relationships with estimated probabilities. Second, given the probabilistic structure predicted by the neural network, we recover a definite B-Rep chain complex by solving a global optimization maximizing the likelihood under structural validness constraints and applying geometric refinements. Extensive tests on large scale CAD datasets demonstrate that the modeling of B-Rep chain complex structure enables more accurate detection for learning and more constrained reconstruction for optimization, leading to structurally more faithful and complete CAD B-Rep models than previous results.\n          <\/jats:p>","DOI":"10.1145\/3528223.3530078","type":"journal-article","created":{"date-parts":[[2022,7,22]],"date-time":"2022-07-22T21:06:27Z","timestamp":1658523987000},"page":"1-18","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":84,"title":["ComplexGen"],"prefix":"10.1145","volume":"41","author":[{"given":"Haoxiang","family":"Guo","sequence":"first","affiliation":[{"name":"Tsinghua University, and Microsoft Research Asia, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shilin","family":"Liu","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, and Microsoft Research Asia, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Pan","sequence":"additional","affiliation":[{"name":"Microsoft Research Asia, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Liu","sequence":"additional","affiliation":[{"name":"Microsoft Research Asia, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Tong","sequence":"additional","affiliation":[{"name":"Microsoft Research Asia, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baining","family":"Guo","sequence":"additional","affiliation":[{"name":"Microsoft Research Asia, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,7,22]]},"reference":[{"key":"e_1_2_2_1_1","volume-title":"Geomagic Design X. https:\/\/www.3dsystems.com\/software\/geomagic-design-x Accessed","author":"3D Systems Inc. 2021.","year":"2021","unstructured":"3D Systems Inc. 2021. 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In IEEE Conference on Computer Vision and Pattern Recognition (CVPR)."},{"key":"e_1_2_2_46_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58571-6_16"},{"key":"e_1_2_2_47_1","volume-title":"Variational shape approximation of point set surfaces. Computer Aided Geometric Design 80","author":"Skrodzki Martin","year":"2020","unstructured":"Martin Skrodzki, Eric Zimmermann, and Konrad Polthier. 2020. Variational shape approximation of point set surfaces. Computer Aided Geometric Design 80 (2020)."},{"key":"e_1_2_2_48_1","volume-title":"Advances in Neural Information Processing Systems","volume":"30","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, \u0141 ukasz Kaiser, and Illia Polosukhin. 2017. Attention is All you Need. In Advances in Neural Information Processing Systems, Vol. 30. Curran Associates, Inc."},{"key":"e_1_2_2_49_1","unstructured":"Xiaogang Wang Yuelang Xu Kai Xu Andrea Tagliasacchi Bin Zhou Ali Mahdavi-Amiri and Hao Zhang. 2020. PIE-NET: Parametric Inference of Point Cloud Edges. In Neural Information Processing Systems."},{"key":"e_1_2_2_50_1","volume-title":"Topological Structures for Geometric Modeling","author":"Weiler Kevin J.","unstructured":"Kevin J. Weiler. 1986. Topological Structures for Geometric Modeling. Rensselaer Polytechnic Institute."},{"key":"e_1_2_2_51_1","volume-title":"Shape Reconstruction Incorporating Multiple Nonlinear Geometric Constraints. Constraints 7 (04","author":"Werghi Naoufel","year":"2002","unstructured":"Naoufel Werghi, Robert Fisher, Anthony Ashbrook, and Craig Robertson. 2002. Shape Reconstruction Incorporating Multiple Nonlinear Geometric Constraints. Constraints 7 (04 2002)."},{"key":"e_1_2_2_52_1","volume-title":"Hang Chu, Yunsheng Tian, Yifei Li, Daniele Grandi, Aditya Sanghi, Linh Tran, J. 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