Segmentation of structural parts of rosebush plants with 3D point-based deep learning methods

TL;DR

This study evaluates six point-based deep learning architectures for segmenting structural parts of rosebush plants in 3D models, demonstrating the effectiveness of synthetic data pre-training and highlighting PointNet++'s superior performance.

Contribution

It adapts and compares six recent deep learning architectures for plant segmentation, utilizing synthetic data for pre-training to improve accuracy on real plant models.

Findings

PointNet++ achieves the highest segmentation accuracy.

Synthetic data pre-training enhances model performance.

Pre-training benefits vary across architectures.

Abstract

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.