Conference Information

GMP 2025: Geometric Modeling and Processing

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Submission Date:
2024-12-09
Notification Date:
2025-04-14
Conference Date:
2025-05-28
Location:
St. Louis, Missouri, USA
Years:
19
CCF: c   CORE: b   QUALIS: b2   Viewed: 57475   Tracked: 22   Attend: 7

Call For Papers

GMP 2025 (Geometric Modeling and Processing) is a CCF C / CORE B / QUALIS B2 conference held in St. Louis, Missouri, USA on 2025-05-28. The paper submission deadline is 2024-12-09. Acceptance notifications are sent on 2025-04-14.

GMP is an annual international conference series on geometric modeling, simulation, and computing. The modeling and processing of geometric data is fundamental to many computer applications, including computer graphics, computer vision, CAD/CAM, medical imaging, engineering analysis, robotics, additive manufacturing, and scientific computing. The GMP conference series provides researchers and practitioners with a forum for exchanging new ideas, discussing new applications, and presenting new solutions. GMP is one of the three flagship conferences held by ASIAGRAPHICS. GMP 2025 will be held in-person on the beautiful campus of Washington University in St. Louis. The meeting venue will be located in the McKelvey School of Engineering. The previous GMP conferences were held in Qingdao (2024), Genova (2023), online (2020-2022), Vancouver (2019), Aachen (2018), Xiamen (2017), and San Antonio (2016), etc., with great success. GMP 2025 will take place in St. Louis, USA, from May 28 to 30, 2025. The organizers of GMP 2025 invite submissions of full-length papers on topics including, but not limited, to: Computational design, manufacturing and 3D printing Computational geometry and topology Discrete differential geometry Geometric content generation Geometric feature modeling and recognition Geometric learning/data-driven approaches Isogeometric analysis Image based modeling Material modeling Mathematical foundations of computer aided geometric design Multi-resolution and heterogeneous modeling Shape and solid representation Shape optimization
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