PARTNER: Level up the Polar Representation for LiDAR 3D Object Detection

PARTNER: Level up the Polar Representation for LiDAR 3D Object Detection,
Ming Nie, Yujing Xue, Chunwei Wang, Chaoqiang Ye, Hang Xu, Xinge Zhu, Qingqiu Huang, Michael Bi Mi, Xinchao Wang, Li Zhang
ICCV 2023

This is a official implementation of ICCV 2023 paper PARTNER: Level up the Polar Representation for LiDAR 3D Object Detection for polar-based 3D object detection.

Introduction

PARTNER alleviates the dilemma of feature distortion with global representation re-alignment and facilitates the regression by introducing instance-level geometric information into the detection head.

We provide code and training configurations of PARTNER under configs.

Requirements

The codes are tested in the following environment:

Ubuntu 18.04
Python 3.7+
PyTorch 1.6+
CUDA 10.2+
APEX
spconv

Installation

a. Clone this repository.

b. Install the dependent libraries as follows:

Install the dependent python libraries:

pip install -r requirements.txt

Install the APEX library:

git clone https://github.com/NVIDIA/apex
cd apex
pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./

Install the SparseConv library, we use the implementation from [spconv].

pip install spconv-cu114

c. Compile CUDA operators by running the following command:

python setup.py develop

Training

We provide training configurations of PARTNER under configs.
All the models are trained with Tesla V100 GPUs (32G). If you use different number of GPUs for training, it's necessary to change the respective training epochs to attain a decent performance.

Use the following command to start a distributed training using 8 GPUs. The models and logs will be saved to work_dirs/CONFIG_NAME.

python -m torch.distributed.launch --nproc_per_node=8 ./tools/train.py CONFIG_PATH

For distributed testing with 8 gpus,

python -m torch.distributed.launch --nproc_per_node=8 ./tools/dist_test.py CONFIG_PATH --work_dir work_dirs/CONFIG_NAME --checkpoint work_dirs/CONFIG_NAME/latest.pth

BibTeX

If you find our work useful in your research, please consider citing our paper:

@inproceedings{nie2023partner,
  title={PARTNER: Level up the Polar Representation for LiDAR 3D Object Detection},
  author={Nie, Ming and Xue, Yujing and Wang, Chunwei and Ye, Chaoqiang and Xu, Hang and Zhu, Xinge and Huang, Qingqiu and Mi, Michael Bi and Wang, Xinchao and Zhang, Li},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  year={2023}
}

Acknowledgements

We thanks for the opensource codebases, det3d and polarstream.