  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.
