Robotic branch pruning is a significantly growing research area to cope with the shortage of labor force in the context of agriculture. One fundamental requirement in robotic pruning is the perception of detailed geometry and topology of branches. However, the point clouds obtained in agricultural settings often exhibit incompleteness due to several constraints, thereby restricting the accuracy of downstream robotic pruning. In this work, we addressed the issue of point cloud quality through a simulation-based deep neural network, leveraging a Real-to-Simulation (Real2Sim) data generation pipeline that not only eliminates the need for manual parameterization but also guarantees the realism of simulated data. The simulation-based neural network was applied to jointly perform point cloud completion and skeletonization on real-world partial branches, without additional real-world training. The Sim2Real qualitative completion and skeletonization results showed the model's remarkable capability for geometry reconstruction and topology prediction. Additionally, we quantitatively evaluated the Sim2Real performance by comparing branch-level trait characterization errors using raw incomplete data and complete data. The Mean Absolute Error (MAE) reduced by 75% and 8% for branch diameter and branch angle estimation, respectively, using the best complete data, which indicates the effectiveness of the Real2Sim data in a zero-shot generalization setting. The characterization improvements contributed to the precision and efficacy of robotic branch pruning.

翻译：机器人枝干修剪是应对农业劳动力短缺的重要研究方向。机器人修剪的一个基本要求是对枝干精细几何结构与拓扑关系的感知。然而，农业场景中获取的点云常因多种限制存在不完整性问题，从而制约了下游机器人修剪的精度。本研究通过基于仿真的深度神经网络解决点云质量问题，利用"真实到仿真"数据生成流程，该流程不仅无需人工参数化，还能保证仿真数据的真实性。该基于仿真的神经网络被应用于对真实世界局部枝干进行点云补全与骨架化联合处理，且无需额外的真实世界训练。仿真到真实的定性补全与骨架化结果表明，该模型在几何重建与拓扑预测方面具有突出能力。此外，我们通过对比使用原始不完整数据与完整数据时枝干级性状表征的误差，对仿真到真实的性能进行了定量评估。使用最优完整数据时，枝干直径与枝干角度估计的平均绝对误差分别降低了75%与8%，这表明"真实到仿真"数据在零样本泛化场景中的有效性。表征精度的提升有助于提高机器人枝干修剪的精确性与效能。