The production and availability of annotated data sets are indispensable for training and evaluation of automatic phenotyping methods. The need for complete 3D models of real plants with organ-level labeling is even more pronounced due to the advances in 3D vision-based phenotyping techniques and the difficulty of full annotation of the intricate 3D plant structure. We introduce the ROSE-X data set of 11 annotated 3D models of real rosebush plants acquired through X-ray tomography and presented both in volumetric form and as point clouds. The annotation is performed manually to provide ground truth data in the form of organ labels for the voxels corresponding to the plant shoot. This data set is constructed to serve both as training data for supervised learning methods performing organ-level segmentation and as a benchmark to evaluate their performance. The rosebush models in the data set are of high quality and complex architecture with organs frequently touching each other posing a challenge for the current plant organ segmentation methods. We report leaf/stem segmentation results obtained using four baseline methods. The best performance is achieved by the volumetric approach where local features are trained with a random forest classifier, giving Intersection of Union (IoU) values of 97.93% and 86.23% for leaf and stem classes, respectively. We provided an annotated 3D data set of 11 rosebush plants for training and evaluation of organ segmentation methods. We also reported leaf/stem segmentation results of baseline methods, which are open to improvement. The data set, together with the baseline results, has the potential of becoming a significant resource for future studies on automatic plant phenotyping.

中文翻译：

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ROSE-X：用于评估 3D 植物器官分割方法的带注释数据集


带注释的数据集的产生和可用性对于自动表型分析方法的培训和评估是必不可少的。由于基于 3D 视觉的表型分析技术的进步以及对复杂的 3D 植物结构进行完整注释的困难，对具有器官级标记的真实植物的完整 3D 模型的需求更加明显。我们介绍了 ROSE-X 数据集，其中包含通过 X 射线断层扫描获取的 11 个带注释的真实玫瑰植物 3D 模型，并以体积形式和点云形式呈现。手动执行注释，以与植物芽相对应的体素的器官标签的形式提供地面实况数据。该数据集的构建既可以作为执行器官级分割的监督学习方法的训练数据，也可以作为评估其性能的基准。数据集中的蔷薇模型质量高、结构复杂，器官之间频繁接触，对当前的植物器官分割方法提出了挑战。我们报告使用四种基线方法获得的叶/茎分割结果。最佳性能是通过体积方法实现的，其中使用随机森林分类器训练局部特征，叶类和茎类的并集交集 (IoU) 值分别为 97.93% 和 86.23%。我们提供了 11 种蔷薇植物的带注释 3D 数据集，用于器官分割方法的训练和评估。我们还报告了基线方法的叶/茎分割结果，这些结果有待改进。该数据集以及基线结果有可能成为未来自动植物表型研究的重要资源。