Awesome

3D Siamese Transformer Network for Single Object Tracking on Point Clouds

Introduction

This repository is released for STNet in our ECCV 2022 paper (poster).

Note: The overall organization of the code is highly similar to our previous code on V2B.

Environment settings

• Create an environment for STNet

conda create -n STNet python=3.7
conda activate STNet

• Install pytorch and torchvision

conda install pytorch==1.7.0 torchvision==0.5.0 cudatoolkit=10.0

• Install dependencies.

pip install -r requirements.txt

Data preparation

KITTI dataset

• Download the velodyne, calib and label_02 from KITTI Tracking. Unzip the downloaded files and place them under the same parent folder.

nuScenes dataset

• The code we have provided in V2B.

• Download the Full dataset (v1.0) from nuScenes.

  Note that base on the offical code nuscenes-devkit, we modify and use it to convert nuScenes format to KITTI format. It requires metadata from nuScenes-lidarseg. Thus, you should replace category.json and lidarseg.json in the Full dataset (v1.0). We provide these two json files in the nuscenes_json folder.

  Executing the following code to convert nuScenes format to KITTI format

  cd nuscenes-devkit-master/python-sdk/nuscenes/scripts
  python export_kitti.py --nusc_dir=<nuScenes dataset path> --nusc_kitti_dir=<output dir> --split=<dataset split>
  

  Note that the parameter of "split" should be "train_track" or "val". In our paper, we use the model trained on the KITTI dataset to evaluate the generalization of the model on the nuScenes dataset.

Waymo open dataset

• We follow the benchmark created by LiDAR-SOT based on the waymo open dataset. You can download and process the waymo dataset as guided by their code, and use our code to test model performance on this benchmark.
• The benchmark they built have many things that we don't use, but the following processing results are necessary:

[waymo_sot]
    [benchmark]
        [validation]
            [vehicle]
                bench_list.json
                easy.json
                medium.json
                hard.json
            [pedestrian]
                bench_list.json
                easy.json
                medium.json
                hard.json
    [pc]
        [raw_pc]
            Here are some segment.npz files containing raw point cloud data
    [gt_info]
        Here are some segment.npz files containing tracklet and bbox data

Node: After you get the dataset, please modify the path variable data_dir&val_data_dir about the dataset under configuration file ./utils/options.

Evaluation

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

For more preset parameters or command debugging parameters, please refer to the relevant code and change it according to your needs.

Recommendations:

• We have provided some pre-trained models under ./results folder, you can use and test them directly.
• Since both kitti and waymo are datasets constructed from 64-line LiDAR, nuScenes is a 32-line LiDAR. We recommend you: train your model on KITTI and verify the generalization ability of your model on waymo. Train on nuScenes or simply skip this dataset. We do not recommend that you verify the generalization ability of your model on nuScenes.

Citation

If you find the code or trained models useful, please consider citing:

@inproceedings{hui2022stnet,
  title={3D Siamese Transformer Network for Single Object Tracking on Point Clouds},
  author={Hui, Le and Wang, Lingpeng and Tang, Linghua and Lan, Kaihao and Xie, Jin and Yang, Jian},
  booktitle={ECCV},
  year={2022}
}
@inproceedings{hui2021v2b,
  title={3D Siamese Voxel-to-BEV Tracker for Sparse Point Clouds},
  author={Hui, Le and Wang, Lingpeng and Cheng, Mingmei and Xie, Jin and Yang, Jian},
  booktitle={NeurIPS},
  year={2021}
}

Acknowledgements

• Thank Qi for his implementation of P2B.
• Thank Pang for the 3D-SOT benchmark based on the waymo open dataset.

License

This repository is released under MIT License.