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PTT: PointTrackTransformer

Overview

• Introduction
• Performance
• Setup
• QuickStart
• Acknowledgment
• Citation

Introduction

This is the official code release of "PTT: Point-Track-Transformer Module for 3D Single Object Trackingin Point Clouds"(Accepted as Contributed paper in IROS 2021). :star2: :star2: :star2:

conference paper | video(youtube) | video(bilibili)

This work is towards the point-based 3D SOT (Single Object Tracking) task, and is dedicated to solving several challenges brought by the natural sparsity of point cloud, such as: error accumulation, sparsity sensitivity, and feature ambiguity.

To this end, we proposed our PTT, a framework combining transformer and tracking pipeline. The main pipeline of PTT is as following. Experiments show that tracker can well achieve robust tracking in sparse point cloud scenes (less than 50 foreground points) by using Transformer's Self Attention to re-weight sparse features.

Performance

Here, we show the latest performance of our PTT. In order to better open source our code, we reconstruct the code and optimized some parameters compared to the version in the paper. It is worth noting that we unified the environment and parameter settings of the final version, so the model performance is slightly different from the paper. The performances after code reconstruction are as follows:

kitti dataset

nuScenes dataset

For nuScenes, we follow the settings of BAT to retrain and test our model. And these results are all trained with batchsize 48 on a single Nvidia RTX 3090, while the results of extended journal paper are trained with 8 x 2080Ti GPUs.

Setup

installation

0. install some dependences

   apt update && apt-get install git libgl1 -y
   

1. create conda env and install python 3.8

   conda create -n ptt python=3.8 -y
   conda activate ptt
   git clone https://github.com/shanjiayao/PTT
   cd PTT/
   

2. install torch

   pip install torch==1.7.1+cu110 torchvision==0.8.2+cu110 torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html
   

   It is worth noting that we tested our code on different versions of cuda, and finally found that the performance will be different due to the randomness of the cuda version. So please use cuda version at least 11.0 and install torch follow the above command.

3. install others

   pip install -r requirements.txt
   conda install protobuf -y
   

4. [optional] install visualize tools

   pip install vtk==9.0.1
   pip install mayavi==4.7.4 pyqt5==5.15.6
   

5. setup ptt package

   python setup.py develop   # ensure be root dir
   

dataset configuration

1. Kitti

   Download the dataset from KITTI Tracking and organize the downloaded files as follows:

   PTT                                           
   |-- data                                     
   |   |-- kitti                                                                          
   │   │   └── training
   │   │       ├── calib
   │   │       ├── label_02
   │   │       └── velodyne
   
   

2. nuScenes

   Download the dataset from nuScenes and organize the downloaded files as follows:

   PTT                                           
   |-- data              
   |   └── nuScenes                                                      
   |       |── maps
   |       |── samples
   |       |── sweeps
   |       └── v1.0-trainval
   

QuickStart

configs

The model configs are located within tools/cfgs for different datasets. Please refer to ptt.yaml to learn more introduction about the model configs.

pretrained models

Here we provide the pretrained models on both kitti and nuscenes dataset. You can download these models from google drive. Then organize the downloaded files as follows:

PTT
├── output
│   ├── kitti_models
│   └── nuscenes_models

train

For training, you can customize the training by modifying the parameters in the yaml file of the corresponding model, such as 'CLASS_NAMES', 'OPTIMIZATION', 'TRAIN' and 'TEST'.

After configuring the yaml file, run the following command to parser the path of config file and the training tag.

cd PTT/tools
# python train_tracking.py --cfg_file cfgs/kitti_models/ptt.yaml --extra_tag car
python train_tracking.py --cfg_file $model_config_path --extra_tag $your_train_tag

By default, we use a single Nvidia RTX 3090 for training.

For training with ddp, you can execute the following command ( ensure be root dir ):

# bash scripts/train_ddp.sh 2 --cfg_file cfgs/kitti_models/ptt.yaml --extra_tag car
bash scripts/train_ddp.sh $NUM_GPUs --cfg_file $model_config_path --extra_tag $your_train_tag

eval

Similar to training, you need to configure parameters such as 'CLASS_NAMES' in the yaml file first, and then run the following commands to test single checkpoint.

cd PTT/tools
# python test_tracking.py --cfg_file cfgs/kitti_models/ptt.yaml --extra_tag car --ckpt ../output/kitti_models/ptt/car/ckpt/best_model.pth
python test_tracking.py --cfg_file $model_config_path --extra_tag $your_train_tag --ckpt $your_saved_ckpt

If you need to test all models, you could modify the default value of 'eval_all' in here before running above command.

After evaluation, the results are saved to the same path as the model, such as 'output/kitti_models/ptt/car/'.

Acknowledgment

• This repo is built upon P2B and OpenPCDet.
• Thank Ghostish for his implementation of BAT.
• Thank qq456cvb for his implementation of Point-Transformers.

Citation

If you find the project useful for your research, you may cite,

@INPROCEEDINGS{ptt,
  author={Shan, Jiayao and Zhou, Sifan and Fang, Zheng and Cui, Yubo},
  booktitle={2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, 
  title={PTT: Point-Track-Transformer Module for 3D Single Object Tracking in Point Clouds}, 
  year={2021},
  volume={},
  number={},
  pages={1310-1316},
  doi={10.1109/IROS51168.2021.9636821}}

@ARTICLE{ptt-journal,
  author={Jiayao, Shan and Zhou, Sifan and Cui, Yubo and Fang, Zheng},
  journal={IEEE Transactions on Multimedia}, 
  title={Real-time 3D Single Object Tracking with Transformer}, 
  year={2022},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/TMM.2022.3146714}}