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Graph."],"published-print":{"date-parts":[[2025,12]]},"abstract":"<jats:p>Boundary representation (B-rep) is the de facto standard for CAD model representation in modern industrial design. The intricate coupling between geometric and topological elements in B-rep structures has forced existing generative methods to rely on cascaded multi-stage networks, resulting in error accumulation and computational inefficiency. We present BrepGPT, a single-stage autoregressive framework for B-rep generation. Our key innovation lies in the Voronoi Half-Patch (VHP) representation, which decomposes B-reps into unified local units by assigning geometry to nearest half-edges and sampling their next pointers. Unlike hierarchical representations that require multiple distinct encodings for different structural levels, our VHP representation facilitates unifying geometric attributes and topological relations in a single, coherent format. We further leverage dual VQ-VAEs to encode both vertex topology and Voronoi Half-Patches into vertex-based tokens, achieving a more compact sequential encoding. A decoder-only Transformer is then trained to autoregressively predict these tokens, which are subsequently mapped to vertex-based features and decoded into complete B-rep models. Experiments demonstrate that BrepGPT achieves state-of-the-art performance in unconditional B-rep generation. The framework also exhibits versatility in various applications, including conditional generation from category labels, point clouds, text descriptions, and images, as well as B-rep autocompletion and interpolation.<\/jats:p>","DOI":"10.1145\/3763323","type":"journal-article","created":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T17:15:39Z","timestamp":1764868539000},"page":"1-18","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["BrepGPT: Autoregressive B-rep Generation with Voronoi Half-Patch"],"prefix":"10.1145","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-1060-689X","authenticated-orcid":false,"given":"Pu","family":"Li","sequence":"first","affiliation":[{"name":"MAIS, Institute of Automation, Chinese Academy of Sciences, Beijing, China"},{"name":"School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-1654-3462","authenticated-orcid":false,"given":"Wenhao","family":"Zhang","sequence":"additional","affiliation":[{"name":"MAIS, Institute of Automation, Chinese Academy of Sciences, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0892-581X","authenticated-orcid":false,"given":"Weize","family":"Quan","sequence":"additional","affiliation":[{"name":"MAIS, Institute of Automation, Chinese Academy of Sciences, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5685-6092","authenticated-orcid":false,"given":"Biao","family":"Zhang","sequence":"additional","affiliation":[{"name":"King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0627-9746","authenticated-orcid":false,"given":"Peter","family":"Wonka","sequence":"additional","affiliation":[{"name":"King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2209-2404","authenticated-orcid":false,"given":"Dongming","family":"Yan","sequence":"additional","affiliation":[{"name":"MAIS, Institute of Automation, Chinese Academy of Sciences, Beijing, China"},{"name":"School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2025,12,4]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/325334.325218"},{"key":"e_1_2_1_2_1","doi-asserted-by":"crossref","first-page":"011007","DOI":"10.1115\/1.4063226","article-title":"HGCAD: hierarchical graph learning for material prediction and recommendation in computer-aided design","volume":"24","author":"Bian Shijie","year":"2024","unstructured":"Shijie Bian, Daniele Grandi, Tianyang Liu, Pradeep Kumar Jayaraman, Karl Willis, Elliot Sadler, Bodia Borijin, Thomas Lu, Richard Otis, Nhut Ho, et al. 2024. 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