CAD: Computer Aided Design

Updated: 2016/11/10   

Scientific leaders: Dr. Jean-Claude PAUL (Inria); Dr. Junhai YONG (School of Software of Tsinghua University)

LIAMA founding members involved: Inria (France), Tsinghua(China), CASIA (China)

Other partners: Girona University(Spain), Konstanz University (Germany)

Host: Tsinghua

Creation: 2004


Geometry Modeling has a dramatic impact on the way designer and engineer work. In the industry, sketch and design, structural and mechanical engineering, aerodynamic studies, marketing, project review, pilot training, ergonomic studies or maintenance operations, all these works are based on geometric models and numerical simulations on these models. In Computer Aided Systems, the mathematical representation of curves and surfaces are based on parametric surfaces. This representation is very practical for the designer. The designer can create curves and surfaces very easily with control points and basis functions that influence the domain of control points. However, there are a lot of theoretical and practical computational problems with these surfaces. Our overall objective in CAD is, based on new Differential Geometry contributions, to find original ways to address these problems. Since 2010, the team also worked in Computer Graphics, especially in Computational Photography, Rendering and Computer Animation. Currently, the Group works in the direction of using Machine Learning methods to solve Computer Graphics problems, with applications in Design.



In Computer Aided Design, our main results addressed Geometry Continuity and a new kind of surface that provides more Geometry Beautification. Our results were published in the best Journals, notably ACM Transaction on Graphics and The International of Computer-Aided-Design (Elsevier). In Computer Graphics, we addressed the problems of Image resizing and Sampling methods for realistic Rendering. Our contributions were published in ACM Siggraph ASIA, the best International Conference in Computer Graphics. We also built a large scale-modeling framework by generating stochastic shape grammars taking one or multiple deterministic grammars as input.

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