  Segmentation of structural parts of 3D models of plants is an important step
for plant phenotyping, especially for monitoring architectural and
morphological traits. This work introduces a benchmark for assessing the
performance of 3D point-based deep learning methods on organ segmentation of 3D
plant models, specifically rosebush models. Six recent deep learning
architectures that segment 3D point clouds into semantic parts were adapted and
compared. The methods were tested on the ROSE-X data set, containing fully
annotated 3D models of real rosebush plants. The contribution of incorporating
synthetic 3D models generated through Lindenmayer systems into training data
was also investigated.
