This work proposes a novel concept for tree and plant reconstruction by directly inferring a Lindenmayer-System (L-System) word representation from image data in an image captioning approach. We train a model end-to-end which is able to translate given images into L-System words as a description of the displayed tree. To prove this concept, we demonstrate the applicability on 2D tree topologies. Transferred to real image data, this novel idea could lead to more efficient, accurate and semantically meaningful tree and plant reconstruction without using error-prone point cloud extraction, and other processes usually utilized in tree reconstruction. Furthermore, this approach bypasses the need for a predefined L-System grammar and enables species-specific L-System inference without biological knowledge.

翻译：本文提出一种全新的树木与植物重建概念，通过图像描述方法直接从图像数据中推断Lindenmayer系统（L-System）的词语表示。我们训练了一个端到端模型，该模型能够将给定图像转换为描述所展示树木的L-System词语。为验证这一概念，我们在二维树木拓扑结构上展示了其适用性。若将该创新想法迁移至真实图像数据，有望实现更高效、精准且具有语义意义的树木与植物重建，无需使用通常用于树木重建的易出错点云提取及其他过程。此外，该方法无需预定义L-System语法，且能在无需生物学知识的情况下实现物种特异性L-System推断。