Title: Rosetum3D: A Large-Scale 3D Vision Dataset from Preharvest Roses
Primary area: datasets and benchmarks
Keywords: Rosetum3D, 3D Dataset, Rose localization

Abstract: The global rose cultivation industry has experienced continued expansion driven by rising demand for cut flowers and botanical extracts. However, automated pre-harvest quality grading faces significant challenges due to occlusion-heavy canopy environments and morphological variations across growth stages. While 3D computer vision offers solutions for localization tasks, progress has been hindered by the lack of large-scale datasets with phenotypic keypoint annotations that capture plant architecture. To bridge this gap, we introduce Rosetum3D, constructed via occlusion-robust multi-view RGB-D capture protocols in commercial greenhouses. We provide fine-grained 2D localization using bounding boxes and botanically defined keypoints with 3D structures recovered through depth backprojection. Models trained on Rosetum3D have achieved 2D/3D rose localization, a crucial step for automated pre-harvest quality grading and growth monitoring. Beyond localization, Rosetum3D serves as a benchmark for agricultural vision tasks, including 2D rose object detection, local feature matching, and depth estimation. By enabling data-driven precision agriculture, Rosetum3D paves the way for robotic harvesting systems and AI-driven yield prediction in protected cultivation.

TL;DR: We present ​​Rosetum3D​​, the first large-scale multi-view RGB-D dataset for preharvest rose, to enable robust preharvest rose localization and serve as a benchmark for agricultural vision tasks.