论文标题
可区分的迭代功能系统
Differentiable Iterated Function Systems
论文作者
论文摘要
该初步论文在使用可区分的渲染管道呈现迭代功能系统（IFS）分形方面介绍了初始探索。可区分的渲染是计算机图形和机器学习交集的最新创新。由可区分操作组成的分形渲染管道为产生符合特定标准的分形带来了许多可能性。在本文中，我通过生成具有类似给定目标图像的固定点的if分形来证明该管道 - 一个被称为\ emph {iNverse ifs ifs问题}的著名问题。这项工作的主要贡献如下：1）我演示（并提供代码）此渲染管道； 2）我讨论了基于分形结构的基于梯度的优化中的一些细微差别和陷阱； 3）我讨论了解决其中一些陷阱的最佳实践；最后4）我讨论了进一步实验以验证该技术的方向。
This preliminary paper presents initial explorations in rendering Iterated Function System (IFS) fractals using a differentiable rendering pipeline. Differentiable rendering is a recent innovation at the intersection of computer graphics and machine learning. A fractal rendering pipeline composed of differentiable operations opens up many possibilities for generating fractals that meet particular criteria. In this paper I demonstrate this pipeline by generating IFS fractals with fixed points that resemble a given target image - a famous problem known as the \emph{inverse IFS problem}. The main contributions of this work are as follows: 1) I demonstrate (and make code available) this rendering pipeline; 2) I discuss some of the nuances and pitfalls in gradient-descent-based optimization over fractal structures; 3) I discuss best practices to address some of these pitfalls; and finally 4) I discuss directions for further experiments to validate the technique.
