SiCP: Simultaneous Individual and Cooperative Perception for 3D Object Detection in Connected and Automated Vehicles
SiCP is accepted to IROS 2024.
-
Support SOTA detector
-
Support SOTA cooperative perception models
-
Support dataset
- OPV2V [ICRA2022]
- V2V4Real [CVPR2023] (Will relase the code soon)
- Download the OPV2V and V2V4Real datasets.
- After downloading the dataset, place the data into the following structure.
├── opv2v_data_dumping
│ ├── train
│ │ │── 2021_08_22_22_30_58
│ ├── validate
│ ├── test
git clone https://github.com/DarrenQu/SiCP.git
cd SiCP
conda create -n sicp python=3.7
conda activate sicp
conda install pytorch==1.12.0 torchvision==0.13.0 cudatoolkit=11.3 -c pytorch -c conda-forge
pip install spconv-cu113
pip install -r requirements.txt
python setup.py develop
python opencood/utils/setup.py build_ext --inplace
To train the model, run the following command.
python opencood/tools/train.py --hypes_yaml ${CONFIG_FILE} [--model_dir ${CHECKPOINT_FOLDER}]
hypes_yaml
: the path of configuration file, e.g.opencood/hypes_yaml/point_pillar_sicp.yaml
.model_dir
(optional): the path of checkpoint.- More explaination refer to this repo.
First, ensure that the validation_dir
parameter in the config.yaml
file, located in your checkpoint folder, is set to the path of the testing dataset, for example, opv2v_data_dumping/test
.
python opencood/tools/inference.py --model_dir ${CHECKPOINT_FOLDER} --fusion_method ${FUSION_STRATEGY} [--show_vis] [--show_sequence]
model_dir
: the path of saved model.fusion_method
: about the fusion strategy, 'early', 'late', and 'intermediate'.show_vis
: whether to visualize the detection overlay with point cloud.show_sequence
: visualize in a video stream.
This project is impossible without these excellent codebases OpenCOOD, CoAlign and V2V4Real.
@article{qu2023sicp,
title={SiCP: Simultaneous Individual and Cooperative Perception for 3D Object Detection in Connected and Automated Vehicles},
author={Qu, Deyuan and Chen, Qi and Bai, Tianyu and Qin, Andy and Lu, Hongsheng and Fan, Heng and Fu, Song and Yang, Qing},
journal={arXiv preprint arXiv:2312.04822},
year={2023}
}