PV-RCNN

Pytorch implementation of PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection.

Features

• Keypoints sampling
• Voxel set abstraction (VSA)
• Extended VSA
• ROI grid pooling
• SECOND backbone (SpMiddleFHD)
• Predicted keypoint weighting
• IOU-Net
• Training code

Installation

Tested in environment:

• CUDA 10.0
• torch 1.0
• Conda
• Ubuntu 18.04
• Python 3.6

1. Installing Pointnet2:

git clone https://github.com/sshaoshuai/Pointnet2.PyTorch.git
cd Pointnet2.PyTorch && python setup.py install
export PYTHONPATH=$PYTHONPATH:/path/to/Pointnet2.PyTorch/

2. Installing spconv:

git clone https://github.com/traveller59/spconv.git --recursive
cd spconv && git checkout 7342772
python setup.py bdist_wheel
cd ./dist && pip install *.whl

3. Installing pvrcnn (this package):

python setup.py develop