PVRNet: Point-View Relation Neural Network for 3D Shape Recognition (AAAI 2019)

Created by Haoxuan You, Yifan Feng, Xibin Zhao, Changqing Zou, Rongrong Ji, Yue Gao from Tsinghua University.

This work will appear in AAAI 2019. We propose a point-view relation neural network called PVRNet for 3D shape recognition and retrieval. You can chekc our paper for more details.

In this repository, our code and data are released for training our PVRNet on ModelNet40 dataset.

If you find our work useful in your research, please cite our paper:

@article{you2018pvrnet,
title={PVRNet: Point-View Relation Neural Network for 3D Shape Recognition},
author={You, Haoxuan and Feng, Yifan and Zhao, Xibin and Zou, Changqing and Ji, Rongrong and Gao, Yue},
journal={AAAI 2019},
year={2018}
}

Code is tested under the environment of Pytorch 0.4.1, Python 3.6 and CUDA 9.0 on Ubuntu 16.04.

Data: point cloud data and multi-view(12-view) data from ModelNet40 dataset.

Pretrained Model: multi-view part(MVCNN), point cloud part(DGCNN) and PVRNet

• Download data and pretrained ckpt from above links. Create dir for data as well as result, and place them under corresponding dirs(./data/ and ./result/ckpt/).

  mkdir -p data result/ckpt

• Train PVRNet. This would use pretrained MVCNN model and DGCNN model saved in ./result/ckpt:

  python train_pvrnet.py

• If validate the performance of PVRNet with our pretrained model:

  python val_pvrnet.py

  If validate the performance of pretrained MVCNN and DGCNN models:

  python val_mvcnn.py
  python val_pc.py
  

• If you want to train new model for MVCNN and DGCNN:

  python train_mvcnn.py
  python train_pc.py
  

Our code is released under MIT License (see LICENSE file for details).