We propose a layout estimation method for multi-layered ink using a measurement of the line spread function (LSF) and machine learning. The three-dimensional printing market for general consumers focuses on the reproduction of realistic appearance. In particular, for the reproduction of human skin, it is important to control translucency by adopting a multilayer structure. Traditionally, layer design has depended on the experience of designers. We, therefore, developed an efficient layout estimation to provide arbitrary skin color and translucency. In our method, we create multi-layered color patches of human skin and measure the LSF as a metric of translucency, and we employ a neural network trained with the data to estimate the layout. As an evaluation, we measured the LSF from the computer-graphics-created skin and fabricate skin using the estimated layout; evaluation with root-mean-square error showed that we can obtain color and translucency that are close to the target.

我们提出了一种使用线扩展函数 (LSF) 测量和机器学习的多层墨水布局估计方法。面向一般消费者的三维打印市场侧重于逼真外观的再现。特别是对于人体皮肤的再现，通过采用多层结构来控制半透明性很重要。传统上，图层设计依赖于设计师的经验。因此，我们开发了一种有效的布局估计来提供任意的肤色和半透明度。在我们的方法中，我们创建了人体皮肤的多层色块，并将 LSF 测量为半透明度的度量，并且我们使用经过数据训练的神经网络来估计布局。作为评价，我们从计算机图形创建的皮肤中测量 LSF，并使用估计的布局制作皮肤；均方根误差评估表明我们可以获得接近目标的颜色和半透明度。