This repository is the official implementation of our paper "Multi-Correlation Siamese Transformer Network with Dense Connection for 3D Single Object Tracking, IEEE Robotics and Automation Letters, 2023". Our code is based on V2B and SST.
- Create an environment for MCSTN
conda create -n MCSTN python=3.7
conda activate MCSTN
- Install pytorch and torchvision
conda install pytorch==1.9.1 torchvision==0.10.1 torchaudio==0.9.1 cudatoolkit=10.2 -c pytorch
- Install dependencies.
pip install -r requirements.txt
We use the datasets KITTI, nuScenes, and Waymo in the same way as V2B and STNet. Please refer to V2B for the detail of data preparation.
Note: After you get the dataset ready, please modify the path variable data_dir
and val_data_dir
about the dataset under configuration file MCSTN_main/utils/options
.
Train a new model:
python main.py --which_dataset KITTI/NUSCENES --category_name category_name
Test a model:
python main.py --which_dataset KITTI/NUSCENES/WAYMO --category_name category_name --train_test test
Please refer to the relevant code for more details of the parameter setting.
cd MCSTN_main/visualization/
python visual.py
If you find the code or trained models useful, please consider citing:
@ARTICLE{mcstn2023,
author={Feng, Shihao and Liang, Pengpeng and Gao, Jin and Cheng, Erkang},
journal={IEEE Robotics and Automation Letters},
title={Multi-Correlation Siamese Transformer Network With Dense Connection for 3D Single Object Tracking},
year={2023},
volume={8},
number={12},
pages={8066-8073},
doi={10.1109/LRA.2023.3325715}}
- Thank Le Hui et al. for their implementation of V2B.
- Thank Ziqi Pang et al. for the Waymo 3D-SOT benchmark.