We introduce an inverse procedural modeling approach that learns L-system representations of pixel images with branching structures. Our fully automatic model generates a compact set of textual rewriting rules that describe the input. We use deep learning to discover atomic structures such as line segments or branchings. Orientation and scaling of these structures are determined and the detected structures are combined into a tree. The initial representation is analyzed, and repeating parts are encoded into a small grammar by using greedy optimization while the user can control the size of the detected rules. The output is an L-system that represents the input image as a simple text and a set of terminal symbols. We apply our approach to a variety of examples, demonstrate its robustness against noise and blur, and we show that it can detect user sketches and complex input structures.

中文翻译：

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通过推断 L 系统对分支结构进行逆过程建模

我们介绍了一种逆过程建模方法，该方法学习具有分支结构的像素图像的 L 系统表示。我们的全自动模型会生成一组紧凑的文本重写规则来描述输入。我们使用深度学习来发现原子结构，例如线段或分支。确定这些结构的方向和缩放比例，并将检测到的结构组合成一棵树。分析初始表示，并使用贪心优化将重复部分编码为小语法，同时用户可以控制检测到的规则的大小。输出是一个 L 系统，它将输入图像表示为一个简单的文本和一组终端符号。我们将我们的方法应用于各种示例，展示其对噪声和模糊的鲁棒性，