Vector graphics are widely used in graphical designs and have received more and more attention. However, unlike raster images which can be easily obtained, acquiring high-quality vector graphics, typically through automatically converting from raster images remains a significant challenge, especially for more complex images such as photos or artworks. In this paper, we propose SAMVG, a multi-stage model to vectorize raster images into SVG (Scalable Vector Graphics). Firstly, SAMVG uses general image segmentation provided by the Segment-Anything Model and uses a novel filtering method to identify the best dense segmentation map for the entire image. Secondly, SAMVG then identifies missing components and adds more detailed components to the SVG. Through a series of extensive experiments, we demonstrate that SAMVG can produce high quality SVGs in any domain while requiring less computation time and complexity compared to previous state-of-the-art methods.
@article{arxiv.2311.05276,
title = {SAMVG: A Multi-stage Image Vectorization Model with the Segment-Anything Model},
author = {Haokun Zhu and Juang Ian Chong and Teng Hu and Ran Yi and Yu-Kun Lai and Paul L. Rosin},
journal= {arXiv preprint arXiv:2311.05276},
year = {2023}
}