Pyramid R-CNN

This is a reproduced repo of Pyramid R-CNN for 3D object detection.

The code is mainly based on OpenPCDet.


We provide code and training configurations of Pyramid-V/PV on the KITTI and Waymo Open dataset. Checkpoints will not be released. The dataset organization is same with PCDet.


The codes are tested in the following environment:

  • Ubuntu 18.04
  • Python 3.6
  • PyTorch 1.5
  • CUDA 10.1
  • OpenPCDet v0.3.0
  • spconv v1.2.1


a. Clone this repository.

git clone

b. Install the dependent libraries as follows:

  • Install the dependent python libraries:

pip install -r requirements.txt 
  • Install the SparseConv library, we use the implementation from [spconv].
    • If you use PyTorch 1.1, then make sure you install the spconv v1.0 with (commit 8da6f96) instead of the latest one.
    • If you use PyTorch 1.3+, then you need to install the spconv v1.2. As mentioned by the author of spconv, you need to use their docker if you use PyTorch 1.4+.

c. Compile CUDA operators by running the following command:

python develop


We train all the models with 8 Tesla V100 GPU (32Gb), and all the configs (epochs/learning rate/batch size) are for 8-GPU Distributed Data Parallel (DDP) training. Users may change those training parameters if they want to run with different GPU numbers and memories.

  • models

# pyramid_rcnn_pv.yaml: pyramid roi head on the point-voxel backbone
# pyramid_rcnn_v.yaml: pyramid roi head on the spconv u-net backbone
  • DDP training

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 sh scripts/ 8 --cfg_file cfgs/waymo_models/pyramid_rcnn_pv.yaml
  • DDP testing

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 sh scripts/ 8 --cfg_file cfgs/waymo_models/pyramid_rcnn_pv.yaml --eval_all


If you find this project useful in your research, please consider cite:

  title={Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detection},
  author={Mao, Jiageng and Niu, Minzhe and Bai, Haoyue and Liang, Xiaodan and Xu, Hang and Xu, Chunjing},