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 reconstruction 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 employs Bayesian optimization to estimate plant 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 reconstructions 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无数应用。然而，由于严重的遮挡和复杂的几何结构，当前的三维重建方法难以恢复植物的完整形态。本研究提出了一种基于逆向程序建模、通过优化植物形态参数模型来实现农作物三维重建的新方法。我们的方法首先通过拟合神经辐射场来估计深度图，随后采用贝叶斯优化来估计能产生一致深度渲染结果的植物形态参数。所得三维模型完整且具有生物学合理性。我们在真实农田图像数据集上验证了该方法，并证明重建结果可用于多种监测与模拟应用。