  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.
