Abstract
3D phenotyping of plants plays a crucial role for understanding plant growth, yield prediction, and disease control. We present a pipeline capable of generating high-quality 3D reconstructions of individual agricultural plants. To acquire data, a small commercially available UAV captures images of a selected plant. Apart from placing ArUco markers, the entire image acquisition process is fully autonomous, controlled by a self-developed Android application running on the drone's controller. The reconstruction task is particularly challenging due to environmental wind and downwash of the UAV. Our proposed pipeline supports the integration of arbitrary state-of-the-art 3D reconstruction methods. To mitigate errors caused by leaf motion during image capture, we use an iterative method that gradually adjusts the input images through deformation. Motion is estimated using optical flow between the original input images and intermediate 3D reconstructions rendered from the corresponding viewpoints. This alignment gradually reduces scene motion, resulting in a canonical representation. After a few iterations, our pipeline improves the reconstruction of state-of-the-art methods and enables the extraction of high-resolution 3D meshes. We will publicly release the source code of our reconstruction pipeline. Additionally, we provide a dataset consisting of multiple plants from various crops, captured across different points in time.
Abstract (translated)
植物的三维表型分析在理解植物生长、预测产量和控制疾病方面发挥着关键作用。我们提出了一条能够生成单个农业植物高质量三维重建的流水线。为了获取数据，一个小型商用无人机拍摄选定植物的图像。除了放置ArUco标记外，整个图像采集过程完全自主化，由运行在无人机控制器上的自开发Android应用程序控制。由于环境风和无人机下洗的影响，重建任务尤其具有挑战性。我们提出的流水线支持集成任意先进的三维重建方法。为了减轻叶片移动期间拍摄图像时产生的错误，我们采用了一种迭代方法，逐步通过变形调整输入图像。运动是利用原始输入图像与从相应视点渲染的中间三维重建之间的光流进行估计的。这种对齐逐渐减少场景中的运动，从而形成规范表示。经过几次迭代后，我们的流水线提升了现有方法的重建效果，并能够提取高分辨率的3D网格。我们将公开发布我们重建流水线的源代码。此外，我们还提供了一个包含来自不同作物的多株植物的数据集，这些植物在不同的时间点被捕获。
URL
https://arxiv.org/abs/2510.15491
