  Segmentation of structural parts of 3D models of plants is an important step
for plant phenotyping, especially for monitoring architectural and
morphological traits. Current state-of-the art approaches rely on hand-crafted
3D local features for modeling geometric variations in plant structures. While
recent advancements in deep learning on point clouds have the potential of
extracting relevant local and global characteristics, the scarcity of labeled
3D plant data impedes the exploration of this potential. We adapted six recent
point-based deep learning architectures (PointNet, PointNet++, DGCNN, PointCNN,
ShellNet, RIConv) for segmentation of structural parts of rosebush models. We
generated 3D synthetic rosebush models to provide adequate amount of labeled
data for modification and pre-training of these architectures. To evaluate
their performance on real rosebush plants, we used the ROSE-X data set of fully
annotated point cloud models. We provided experiments with and without the
incorporation of synthetic data to demonstrate the potential of point-based
deep learning techniques even with limited labeled data of real plants. The
experimental results show that PointNet++ produces the highest segmentation
accuracy among the six point-based deep learning methods. The advantage of
PointNet++ is that it provides a flexibility in the scales of the hierarchical
organization of the point cloud data. Pre-training with synthetic 3D models
boosted the performance of all architectures, except for PointNet.
