GEOMETRIC DEEP LEARNING FOR SHAPE CORRESPONDENCE IN MASS CUSTOMIZATION

Many industries, such as human-centric product manufacturers, are calling for mass customization with personalized products. One key enabler of mass customization is 3D printing, which makes the flexible design and manufacturing possible. However, personalized designs bring obstacles for the shape matching and analysis, owing to the high complexity and large shape variations. Traditional shape matching methods are limited to shape alignment, which cannot determine the intrinsic invariance of mass customized models. To extract the deformations widely seen in mass customization paradigm and address the issues of alignment methods in shape matching, we redefine the geometry matching problem as a correspondence problem, and solve for the correspondence of all vertices on a queried shape to a reference shape. A state-of-the-art geometric deep learning method is used to learn the correspondence of a set of collected models. Through learning the intrinsic deformations of the products, the underlying variations of the shapes are extracted. We demonstrate the application of the proposed approach in orthodontics industry, and the experimental results show the effectiveness of the proposed method and the defined problem is favorably suitable for shape analysis in mass customization.