The ability to automatically build 3D digital twins of plants from images has countless applications in agriculture, environmental science, robotics, and other fields. However, current 3D reconstruction methods fail to recover complete shapes of plants due to heavy occlusion and complex geometries. In this work, we present a novel method for 3D modeling of agricultural crops based on optimizing a parametric model of plant morphology via inverse procedural modeling. Our method first estimates depth maps by fitting a neural radiance field and then optimizes a specialized loss to estimate morphological parameters that result in consistent depth renderings. The resulting 3D model is complete and biologically plausible. We validate our method on a dataset of real images of agricultural fields, and demonstrate that the reconstructed canopies can be used for a variety of monitoring and simulation applications.

翻译：从图像自动构建植物的三维数字孪生能力在农业、环境科学、机器人学及其他领域具有广泛应用前景。然而，现有三维重建方法因植物严重的遮挡与复杂几何结构而难以恢复其完整形态。本研究提出一种基于逆向程序化建模、通过优化植物形态参数模型实现农作物三维建模的新方法。该方法首先通过拟合神经辐射场估计深度图，随后优化专用损失函数以估计能产生一致深度渲染结果的形态学参数。所得三维模型具备完整性与生物学合理性。我们在真实农田图像数据集上验证了本方法，并证明重建的冠层结构可应用于多种监测与仿真任务。