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Graph."],"published-print":{"date-parts":[[2025,6,30]]},"abstract":"<jats:p>\n            Reverse engineering CAD models from raw geometry is a classic but challenging research problem. In particular, reconstructing the CAD modeling sequence from point clouds provides great interpretability and convenience for editing. Analyzing previous work, we observed that a CAD modeling sequence represented by tokens and processed by a generative model does not have an immediate geometric interpretation. To improve upon this problem, we introduce geometric guidance into the reconstruction network. Our proposed model, PS-CAD, reconstructs the CAD modeling sequence one step at a time as illustrated in Figure\u00a0\n            <jats:xref ref-type=\"fig\">1<\/jats:xref>\n            . At each step, we provide three forms of geometric guidance. First, we provide the geometry of surfaces where the current reconstruction differs from the complete model as a point cloud. This helps the framework to focus on regions that still need work. Second, we use geometric analysis to extract a set of planar prompts, that correspond to candidate surfaces where a CAD extrusion step could be started. Third, we present a step-wise sampling to generate multiple complete candidate CAD modeling steps instead of single-tokens without direct geometric interpretation. Our framework has three major components. Geometric guidance computation extracts the first two types of geometric guidance. Single-step reconstruction computes a single candidate CAD modeling step for each provided prompt. Single-step selection selects among the candidate CAD modeling steps. The process continues until the reconstruction is completed. Our quantitative results show a significant improvement across all metrics. For example, on the dataset DeepCAD, PS-CAD improves upon the best published SOTA method by reducing the geometry errors (CD and HD) by 10%, and the structural error (ECD metric) by about 13%.\n          <\/jats:p>","DOI":"10.1145\/3733595","type":"journal-article","created":{"date-parts":[[2025,5,8]],"date-time":"2025-05-08T11:06:15Z","timestamp":1746702375000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["PS-CAD: Local Geometry Guidance via Prompting and Selection for CAD Reconstruction"],"prefix":"10.1145","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4480-8549","authenticated-orcid":false,"given":"Bingchen","family":"Yang","sequence":"first","affiliation":[{"name":"University of the Chinese Academy of Sciences School of Artificial Intelligence, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7348-5844","authenticated-orcid":false,"given":"Haiyong","family":"Jiang","sequence":"additional","affiliation":[{"name":"University of the Chinese Academy of Sciences School of Artificial Intelligence, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3628-9777","authenticated-orcid":false,"given":"Hao","family":"Pan","sequence":"additional","affiliation":[{"name":"Software school, Tsinghua University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0329-7458","authenticated-orcid":false,"given":"Guosheng","family":"Lin","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1799-3948","authenticated-orcid":false,"given":"Jun","family":"Xiao","sequence":"additional","affiliation":[{"name":"University of the Chinese Academy of Sciences School of Artificial Intelligence, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0627-9746","authenticated-orcid":false,"given":"Peter","family":"Wonka","sequence":"additional","affiliation":[{"name":"KAUST, Thuwal, Saudi Arabia"}]}],"member":"320","published-online":{"date-parts":[[2025,5,29]]},"reference":[{"key":"e_1_3_4_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/J.CAD.2004.01.006"},{"key":"e_1_3_4_3_1","first-page":"31","volume-title":"Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, June 13\u201319, 2020","author":"Deng Boyang","year":"2020","unstructured":"Boyang Deng, Kyle Genova, Soroosh Yazdani, Sofien Bouaziz, Geoffrey E. 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