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 of 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 data folder contains the simulated branch datasets (i.e., NB and FB branches), while Sim2Real folder for simulation-based model training and inference.
MIT License
If you find our work useful in your research, please consider citing:
@article{qiu20243d,
title={3D Branch Point Cloud Completion for Robotic Pruning in Apple Orchards},
author={Qiu, Tian and Zoubi, Alan and Cheng, Lailiang and Jiang, Yu},
journal={arXiv preprint arXiv:2404.05953},
year={2024}
}