Image-Based 3D Shape Generation Used for 3D Printing

Abstract

3D shape design is one of the most vital procedures in addictive manufacturing, especially in the environment of cloud manufacturing. And the consumption of time and energy to design a 3D shape is huge. Our objective is developing a 3D shape generative technique applied in the process of 3D printing model shape design. As generative adversarial networks (GAN) in field of deep learning has the potential to generate 3D shape models based the latent vector sampled from prior latent spaces. We use Conditional GAN as the solution to map image information to 3D printing shapes that satisfy the printable requirements. We evaluate the capability of our model to generate authentic 3D printing shapes across the several classes. Basically, the model could be an alternative as an assistant 3D printing shape designer.