enviroment requirement

• Python 3.6+
• PyTorch 1.3+
• CUDA 9.2+ (If you build PyTorch from source, CUDA 9.0 is also compatible)
• GCC 5+
• MMCV

step 0. Create a conda virtual environment

conda create -n openmmlab python=3.8 -y
conda activate openmmlab

step 1.Install Pytorch following official instruction

On GPU platforms(recommend):

pip3 install torch==1.10.2+cu113 torchvision==0.11.3+cu113 torchaudio==0.10.2+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html

On CPU platforms:

conda install pytorch torchvision cpuonly -c pytorch

Installation

step 0. Install MMEngine, MMCV and MMDetection using MIM.

pip install -U openmim
mim install mmengine
mim install 'mmcv>=2.0.0rc4'
mim install 'mmdet>=3.0.0'

step 1. Install MMDetection3D.

develop and run mmdet3d directly, install it from source(build from source):

git clone https://github.com/open-mmlab/mmdetection3d.git -b dev-1.x
# "-b dev-1.x" means checkout to the `dev-1.x` branch.
cd mmdetection3d
pip install -v -e .
# "-v" means verbose, or more output
# "-e" means installing a project in edtiable mode,
# thus any local modifications made to the code will take effect without reinstallation.

Verification

Verify with point cloud demo

To verify whether MMDetection3D is installed correctly, we provide some sample codes to run an inference demo.

• Step 1. We need to download config and checkpoint files.

mim download mmdet3d --config pointpillars_hv_secfpn_8xb6-160e_kitti-3d-car --dest .

The downloading will take several seconds or more, depending on your network environment. When it is done, you will find two files pointpillars_hv_secfpn_8xb6-160e_kitti-3d-car.py and hv_pointpillars_secfpn_6x8_160e_kitti-3d-car_20220331_134606-d42d15ed.pth in your current folder.

• Step 2. Verify the inference demo. Case a: If you install MMDetection3D from source, just run the following command.

python demo/pcd_demo.py demo/data/kitti/000008.bin pointpillars_hv_secfpn_8xb6-160e_kitti-3d-car.py hv_pointpillars_secfpn_6x8_160e_kitti-3d-car_20220331_134606-d42d15ed.pth --show

for example:

cd ~/mmdetection3d/
python demo/pcd_demo.py demo/data/kitti/000008.bin pointpillars_hv_secfpn_8xb6-160e_kitti-3d-car.py hv_pointpillars_secfpn_6x8_160e_kitti-3d-car_20220331_134606-d42d15ed.pth --show


# use the parameter '--show' can get a visualized detection output

Pointcloud format Convert

CARLA outputs .ply by default as a point cloud result, so we should convert the output from '.ply' to '.bin' using format_convert.py