  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.
