Installation

Preparation: A Ubuntu system with GPU with GPU memory larger than 13GB.

Install Nvidia driver and CUDA Toolkit.

$ nvidia-smi  # check driver
$ nvcc --version # check toolkit

Install Python and NumPy. Please make sure your NumPy version is at least 1.18.

Install PyTorch with CUDA -- A version than (PyTorch 1.5.1, CUDA 10.1) 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.

Different from 3DIoUMatch, please install the full version of OpenPCDet since the sampling part needs the tools in OpenPCDet.

Install dependencies:

pip install -r requirements.txt

Datasets

ScanNet

Please follow the instructions in scannet/README.md. using the download script with -o $(pwd) --types _vh_clean_2.ply .aggregation.json _vh_clean_2.0.010000.segs.json .txt options to download data.

SUNRGB-D

Please follow the instructions in sunrgbd/README.md.

Pre-training

Please run:

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

For example:

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

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

Training

Please run:

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

For example, use the downloaded models:

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

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

For example, use the downloaded models:

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

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

For evaluation with IoU optimization, please run:

Please run:

sh scripts/run_eval_opt.sh <GPU_ID> <LOG_DIR> <DATASET> <LABELED_LIST> <CKPT> <OPT_RATE>

The number of steps (of optimization) is by default 10.

Acknowledgements

Our implementation uses code from the following repositories:

• 3DIoUMatch: Leveraging IoU Prediction for Semi-Supervised 3D Object Detection
• Deep Hough Voting for 3D Object Detection in Point Clouds
• SESS: Self-Ensembling Semi-Supervised 3D Object Detection
• Pointnet2/Pointnet++ PyTorch
• OpenPCDet