Astro-VOS: Tracking Supernovae Evolution Using Video Object Segmentation

Joycelyn Chen

[arXiv] [PDF] [Project Page]

Table of Contents

1. Introduction
2. Results
3. Training/inference
4. Citation

Introduction

Understanding the influence of supernovae on the interstellar medium (ISM) is crutial for unraveling the complexities of our Galaxy. Traditional methods, however, are inadequate in accurately capturing the three-dimensional structures of superbubbles formed by supernovae, thus constraining detailed quantitative analysis. To bridge this gap, we utilize 3D magnetohydrodynamic numerical simulations to construct a tailored dataset. Moreover, we develop a video object segmentation model to precisely depict the contours of superbubbles within our 3D dataset, offering an in-depth view of superbubble evolution. Our findings, verified against the principles of Sedov-Taylor theories, highlight the effectiveness of our innovative approach in delivering accurate and comprehensive insights into the ISM dynamics, significantly outperforming traditional astrophysical methods.

Training/inference

First, install the required python packages and datasets following GETTING_STARTED.md.

For training, see TRAINING.md.

For inference, see INFERENCE.md.

Citation

Please cite our paper if you find this repo useful!

and the reference model:

@inproceedings{cheng2022xmem,
  title={{XMem}: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model},
  author={Cheng, Ho Kei and Alexander G. Schwing},
  booktitle={ECCV},
  year={2022}
}