3DIoUMatch

Introduction

News: Our paper has been accepted by CVPR 2021!

This is a pre-release of the code of our paper 3DIoUMatch: Leveraging IoU Prediction for Semi-Supervised 3D Object Detection. (arXiv report here).

In this repository, we provide 3DIoUMatch implementation (with Pytorch) based on VoteNet and SESS, as well as the training and evaluation scripts on SUNRGB-D and ScanNet.

Please refer to our project page for more information.

Citation

If you find our work useful in your research, please consider citing:

@article{wang20203dioumatch,
  title={3DIoUMatch: Leveraging IoU Prediction for Semi-Supervised 3D Object Detection},
  author={Wang, He and Cong, Yezhen and Litany, Or and Gao, Yue and Guibas, Leonidas J},
  journal={arXiv preprint arXiv:2012.04355},
  year={2020}
}

Installation

Install Python -- This repo is tested with Python 3.7.6.

Install NumPy -- This repo is tested with NumPy 1.18.5. Please make sure your NumPy version is at least 1.18.

Install PyTorch with CUDA -- This repo is tested with PyTorch 1.5.1, CUDA 10.1. It may work with newer versions, but that is not guaranteed. A lower version may be problematic.

Install TensorFlow (for TensorBoard) -- This repo is tested with TensorFlow 2.2.0.

Compile the CUDA code for PointNet++, which is used in the backbone network:

cd pointnet2
python setup.py install

If there is a problem, please refer to Pointnet2/Pointnet++ PyTorch

Compile the CUDA code for general 3D IoU calculation in OpenPCDet:

cd OpenPCDet
python setup.py install

I deleted the CUDA kernels except 3D IoU calculation in OpenPCDet for faster installation.

Install dependencies:

pip install -r requirements.txt

Datasets

ScanNet

Please follow the instructions in scannet/README.md.

SUNRGB-D

Please follow the instructions in sunrgbd/README.md.

Download Pre-trained and Trained Models

We provide the pre-trained models of ScanNet 10% and SUNRGB-D 5%.

We also provide the trained models of ScanNet 10% and SUNRGB-D 5%.

You may download the models and put them into ckpts.

We provide 2 data splits of ScanNet 10% and SUNRGB-D 5% in scannet/meta_data and sunrgbd/sunrgbd_trainval, respectively.

Pre-training

Please run:

sh run_pretrain.sh <GPU_ID> <LOG_DIR> <DATASET> <LABELED_LIST>

For example:

sh run_pretrain.sh 0 pretrain_scannet scannet scannetv2_train_0.1.txt

sh run_pretrain.sh 0 pretrain_sunrgbd sunrgbd sunrgbd_v1_train_0.05.txt

Training

Please run:

sh run_train.sh <GPU_ID> <LOG_DIR> <DATASET> <LABELED_LIST> <PRETRAIN_CKPT>

For example, use the downloaded models:

sh run_train.sh 0 train_scannet scannet scannetv2_train_0.1.txt ckpts/scan_0.1_pretrain.tar

sh run_train.sh 0 train_sunrgbd sunrgbd sunrgbd_v1_train_0.05.txt ckpts/sun_0.05_pretrain.tar

You may modify the script by adding --view_stats to load labels on unlabeled data and view the statistics on the unlabeled data (e.g. average IoU, class prediction accuracy).

Evaluation

Please run:

sh run_eval.sh <GPU_ID> <LOG_DIR> <DATASET> <LABELED_LIST> <CKPT>

For example, use the downloaded models:

sh run_eval.sh 0 eval_scannet scannet scannetv2_train_0.1.txt ckpts/scan_0.1.tar

sh run_eval.sh 0 eval_sunrgbd sunrgbd sunrgbd_v1_train_0.05.txt ckpts/sun_0.05.tar

Acknowledgements

Our implementation uses code from the following repositories:

Change log

12/31/2020: Pre-release.

leo-hao/3DIoUMatch