In this paper, we propose a method to estimate ink layer layout used as an input for 3D printer. This method makes it possible to reproduce a 3D printed patch that gives a desired translucency, which is represented as Line Spread Function (LSF) in this study. Deep neural networks of encoder decoder model is used for the estimation. In a previous research, it is reported that machine learning method is effective to formulate the complex relationship between the optical property such as LSF and the ink layer layout in 3D printer. However, it may be difficult to collect data large enough to train a neural network sufficiently. Especially, although 3D printer is getting more and more widespread, the printing process is still time consuming. Therefore, in this research, we prepare the training data, which is the correspondence between LSF and ink layer layout in 3D printer, by simulating it on a computer. MCML was used to perform the simulation. MCML is a method to simulate subsurface scattering of light for multi-layered media. Deep neural network was trained with the simulated data, and evaluated using a CG skin object. The result shows that our proposed method can estimate an appropriate ink layer layout which reproduce the appearance close to the target color and translucency.
Kensuke Fukumoto, Kazuki Nagasawa, Wataru Arai, Kunio Hakkaku, Satoshi Kaneko, Keita Hirai, Norimichi Tsumura, "Estimation of Layered Ink Layout to reproduce desired Translucency of skin in Inkjet 3D Printer using deep neural network trained with synthetic simulated data" in Proc. IS&T 28th Color and Imaging Conf., 2020, pp 321 - 326, https://doi.org/10.2352/issn.2169-2629.2020.28.51