3D Branch Point Cloud Completion for Robotic Pruning in Apple Orchards

Robotic branch pruning, a rapidly growing field addressing labor shortages in agriculture, requires detailed perception of branch geometry and topology. However, point clouds obtained in agricultural settings often lack completeness, limiting pruning accuracy. This work addressed point cloud quality via a closed-loop approach, (Real2Sim)−1. Leveraging a Real-to-Simulation (Real2Sim) data generation pipeline, we generated simulated 3D apple trees based on realistically characterized apple tree information without manual parame- terization. These 3D trees were used to train a simulation-based deep model that jointly performs point cloud completion and skeletonization on real-world partial branches, without extra real-world training. The Sim2Real qualitative results showed the model’s remarkable capability for geometry reconstruc- tion and topology prediction. Additionally, we quantitatively evaluated the Sim2Real performance by comparing branch- level trait characterization errors using raw incomplete data and the best complete data. The Mean Absolute Error (MAE) reduced by 75% and 8% for branch diameter and branch angle estimation, respectively, 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.