TomatoWUR: an annotated dataset of 3D tomato plants to quantitatively evaluate segmentation, skeletonisation, and plant trait extraction algorithms for 3D plant phenotyping
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In this research, we created a dataset of 44 annotated 3D point clouds of tomato plants. Images were captured using fifteen cameras surrounding a single plant. Those images were used to create a point cloud using the shape-from silhouette methodology (Golbach et al., 2016). The resulting dataset was annotated and separated into three parts. 1) Annotated point clouds and corresponding RGB images with semantic and instance labels , 2) annotated skeletons to analyse plant architecture, and 3) manual reference measurements of internode length, internode diameter, leaf angle, and phyllotactic angle to evaluate phenotyping algorithms from point cloud to plant traits. ################################# Changes ##########################2025-04-30: v1/v2 upload of all annotated data related to data in brief paper.2026-05-26: v3 Uploaded new annotations versions related to paper "From point clouds to plant traits: investigation of a pipeline for phenotyping of tomato plants using skeletonization"See following new ann_versions: 0-paper-2Dto3D_improved, 1-paper3-swin3d-inference, 1-paper3-swin3d-inference-train[fold_x]Added readme in TomatoWUR/ann_versions/
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