bhuppenthal/treepartnet

This is the code repository implementing the paper "TreePartNet: Neural Decomposition of Point Clouds for 3D Tree Reconstruction".

TreePartNet

This is the code repository implementing the paper "TreePartNet: Neural Decomposition of Point Clouds for 3D Tree Reconstruction".

Dependency and Setup

The project is tested on Ubuntu 18.04 with cuda10.1.

Requirements:

• python==3.7
• pytorch==1.5.0
• pytorch-lightning==0.8.5

The PointNet++ pytorch implementation is modified from Pointnet2_Pytorch. Install dependencies:

pip install -r requirements.txt

Data

The gravity direction in tree point cloud is down along y-axis! All tree point cloud are normalized. see the code in utils for more details.

• Dataset for foliage segmentation: 16K points per tree, hdf5 format, Download Link, Baiduyun for people in China(code:nva1)
• Dataset for neural decomposition: 8K points per tree, hdf5 format, Download Link, Baiduyun for people in China(code:4f5n)

Training

After downloading the data and put them in data folder, the foliage segmentation network can be trained as

python train_foliage.py

and the TreePartNet can be trained using

python train.py

The hyperparameters can be modified in these 2 python files.

Testing

The trained checkpoints can be found in dir fckpt and ckpt. To predict foliage segmentation on test data set above:

python test_foliage.py

and neural decomposition:

python test.py

Reference

If you find our work useful in your research, please cite us using the following BibTeX entry.

@article{TreePartNet21,
title={TreePartNet: Neural Decomposition of Point Clouds for 3D Tree Reconstruction},
author={Yanchao Liu and Jianwei Guo and Bedrich Benes and Oliver Deussen and Xiaopeng Zhang and Hui Huang},
journal={ACM Transactions on Graphics (Proceedings of SIGGRAPH ASIA)},
volume={40},
number={6},
pages={232:1--232:16},
year={2021},
}