论文标题
物理体现的深层图像优化
Physically Embodied Deep Image Optimisation
论文作者
论文摘要
通过学习程序来控制图形机器人，可以创建物理草图。可区分的栅格式使用者用于优化绘图笔划集以匹配输入图像，并使用深网提供一个编码，我们可以计算损失。然后，可以将优化的图形原始图转换为G代码命令，该命令使用机器人使用绘图仪器（例如物理支撑介质上的笔和铅笔）来绘制图像。
Physical sketches are created by learning programs to control a drawing robot. A differentiable rasteriser is used to optimise sets of drawing strokes to match an input image, using deep networks to provide an encoding for which we can compute a loss. The optimised drawing primitives can then be translated into G-code commands which command a robot to draw the image using drawing instruments such as pens and pencils on a physical support medium.
