Class-Level Confidence Based 3D Semi-Supervised Learning

This is a Pytorch implementation of Detectaion part of Class-Level Confidence Based 3D Semi-Supervised Learning.

Paper link: https://arxiv.org/abs/2210.10138

Installation

Preparation: A Ubuntu system with GPU.

Install Nvidia driver and CUDA Toolkit.

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

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.

pip install torch==1.5.1 torchvision==0.6.1

or

conda install pytorch==1.5.1 torchvision==0.6.1 cudatoolkit=10.1 -c pytorch

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 develop

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

Install dependencies:

pip install -r requirements.txt

Installation on Windows

Preparation: GPU support CUDA 10.2, not use GeForce 30 series with Ampere micro-architecture, anaconda is recommended.

> nvidia-smi # check nvidia driver
> nvcc --version # check CUDA Toolkit version
> cl # check MSVC compiler, if no, add compile to environment path

1. create a virtual environment: conda create --name iou python=3.7
2. install numpy: conda install numpy -y
3. install pytorch and cudatoolkit: conda install pytorch==1.5.1 torchvision==0.6.1 cudatoolkit=10.2 -c pytorch
4. degrade setuptools: pip install setuptools=59.6
5. install pointnet2: cd pointnet2; python ./setup.py install
6. install OpenPCDet: cd OpenPCDet; python ./setup.py develop
7. install tensorflow: pip install tensorflow
8. install requirements: 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 without resample

Please run:

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

For example:

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

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

Pre-training with resample (please use the pretrained model obtained above for training)

Please run:

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

For example:

sh run_pretrain.sh 0 pretrain_scannet scannet scannetv2_train_0.1.txt ckpts/scan_0.1_pretrain_ori.tar

sh run_pretrain.sh 0 pretrain_sunrgbd sunrgbd sunrgbd_v1_train_0.05.txt ckpts/sun_0.05_pretrain_ori.tar

Training

Please run:
```shell script
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