A Lightweight and Detector-free 3D Single Object Tracker on Point Clouds (DMT)

Pytorch-Lightning implementation of DMT.

A Lightweight and Detector-free 3D Single Object Tracker on Point Clouds.

IEEE Transactions on Intelligent Transportation Systems 2023

Features

• Modular design. It is easy to config the model and trainng/testing behaviors through just a .yaml file.
• DDP support for both training and testing.

Setup

Installation

• Create the environment

  git clone https://github.com/jimmy-dq/EXPL_BAT.git
  cd BAT
  conda create -n bat  python=3.6
  conda activate bat
  

• Install pytorch

  conda install pytorch==1.4.0 torchvision==0.5.0 cudatoolkit=10.1 -c pytorch
  

  Our code is well tested with pytorch 1.4.0 and CUDA 10.1. But other platforms may also work. Follow this to install another version of pytorch.

• Install other dependencies

  pip install -r requirement.txt
  

KITTI dataset

• Download the data for velodyne, calib and label_02 from KITTI Tracking.
• Unzip the downloaded files.
• Put the unzipped files under the same folder as following.

  [Parent Folder]
  --> [calib]
      --> {0000-0020}.txt
  --> [label_02]
      --> {0000-0020}.txt
  --> [velodyne]
      --> [0000-0020] folders with velodynes .bin files
  

Quick Start

Training

To train a model, you must specify the .yaml file with --cfg argument. The .yaml file contains all the configurations of the dataset and the model. Currently, we provide three .yaml files under the cfgs directory. Note: Before running the code, you will need to edit the .yaml file by setting the path argument as the correct root of the dataset.

python main.py --gpu 0 1 --cfg cfgs/BAT_Car.yaml  --batch_size 50 --epoch 60

After you start training, you can start Tensorboard to monitor the training process:

tensorboard --logdir=./ --port=6006

By default, the trainer runs a full evaluation on the full test split after training every epoch. You can set --check_val_every_n_epoch to a larger number to speed up the training.

Testing

To test a trained model, specify the checkpoint location with --checkpoint argument and send the --test flag to the command.

python main.py --gpu 0 1 --cfg cfgs/BAT_Car.yaml  --checkpoint /path/to/checkpoint/xxx.ckpt --test


### Acknowledgment
+ This repo is built upon [P2B](https://github.com/HaozheQi/P2B) and [SC3D](https://github.com/SilvioGiancola/ShapeCompletion3DTracking).
+ Thank Erik Wijmans for his pytorch implementation of [PointNet++](https://github.com/erikwijmans/Pointnet2_PyTorch)

### License
This repository is released under MIT License (see LICENSE file for details).
# DMT_Tracking