This paper presents a design methodology which automates the generation of curves extracted from the point clouds that have been obtained by digitizing the physical objects. The methodology is described on a product belonging to the industry of consumables, respectively a footwear type product that has a complex shape with many curves. The final result is the automated generation of wrapping curves, surfaces and solids according to the characteristics of the customer's foot, and to the preferences for the chosen model, which leads to the development of customized products.
In this study we present a 3D garment design method for scanned human bodies. Firstly, a slicing method is proposed to reconstruct a symmetric body from a scanned human body to make it suitable for garment design. Secondly, a sketch modeling method is used to interactively create the garment surface based on the new body. Thirdly, seam lines are drawn on the garment surface, and then a flattening algorithm is employed to design 2D patterns. Finally, a seam line template is used to design the same style for different bodies. Experiments show that the resulted garment well fits the scanned body. Our method provides an efficient design tool for customizing garments in the fashion industry.
We prove that groups acting geometrically on delta-quasiconvex spaces contain no essential Baumslag-Solitar quotients as subgroups. This implies that they are translation discrete, meaning that the translation numbers of their nontorsion elements are bounded away from zero.