用机器人和3D高斯点云重建植物数字孪生，解决叶片遮挡问题。
Botany-Bot: Digital Twin Monitoring of Occluded and Underleaf Plant Structures with Gaussian Splats
- 双摄像头+机械臂+转台，通过高斯点云建模植物三维结构。
- 叶片分割准确率90.8%，遮挡部位成像成功率77.3%。
- 适合需精细观测茎芽、叶背等隐蔽结构的植物研究者。
商用固定摄像头植物表型系统因叶片遮挡难以捕捉细节。本文提出Botany-Bot系统，利用双目相机、光箱内转台、工业机械臂及3D分割高斯点云模型，构建活体植物的“带标注数字孪生”。同时设计机器人算法，可主动拨动叶片以获取茎芽、叶背/叶面等遮挡区域的高分辨率可索引图像。实验表明，该系统叶片分割准确率达90.8%，叶片检测准确率为86.2%，叶片抬升/推动成功率为77.9%，遮挡区域成像准确率为77.3%。代码、视频与数据集见https://berkeleyautomation.github.io/Botany-Bot/。
原文摘要 · Abstract (English)
Commercial plant phenotyping systems using fixed cameras cannot perceive many plant details due to leaf occlusion. In this paper, we present Botany-Bot, a system for building detailed "annotated digital twins" of living plants using two stereo cameras, a digital turntable inside a lightbox, an industrial robot arm, and 3D segmentated Gaussian Splat models. We also present robot algorithms for manipulating leaves to take high-resolution indexable images of occluded details such as stem buds and the underside/topside of leaves. Results from experiments suggest that Botany-Bot can segment leaves with 90.8% accuracy, detect leaves with 86.2% accuracy, lift/push leaves with 77.9% accuracy, and take detailed overside/underside images with 77.3% accuracy. Code, videos, and datasets are available at https://berkeleyautomation.github.io/Botany-Bot/.
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