README.md

Region-based Pose Tracking

Description

Python bindings for the M3T library are provided to perform real-time 3D object tracking. Moreover, we provide an extension to the M3T library to replace the traditional histogram-based segmentation with deep learning-based alternatives (pixel-wise segmentation using a MLP and line-wise segmentation using a 1D U-Net).

Installation

You first need to clone the repository main branch:

git clone https://github.com/TomRavaud/region_based_pose_tracking.git
cd region_based_pose_tracking

Then, you need to install the required dependencies. The project uses conda to manage the dependencies. You can create a new conda environment using the provided environment.yaml file:

conda env create -f environment.yaml

Finally, you can install our custom packages using the following commands:

pip install ./m3t_bindings
pip install ./m3t_ext

To remove the conda environment, you can use the following command:

conda remove -n pym3t --all

Datasets

Datasets used in this work are RBOT and BCOT. Our code assumes that the datasets are downloaded and extracted in the data folder.

Models' weights

Trained parameters for the segmentation models are to be downloaded and extracted in the weights folder. You can find their latest version here.

In addition, our models make use of the MobileSAM pretrained model. You can download the weights from the MobileSAM repository.

Usage

You can run the provided tracking example (on RBOT) using the following command:

python -m pym3t_ext.scripts.track

Other scripts are provided to run a tracking method on a whole dedicated dataset (pym3t_ext.scripts.evaluate) and to compute performance scores at each frame (pym3t_ext.scripts.compute_metrics). Each script comes with its own set of parameters defined in the configs folder.

Note that if you intend to run the scripts headless, you can use the tool Xvfb:

Xvfb :1 -screen 0 640x480x24 &
export DISPLAY=:1