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Vector graphics are widely used in graphical designs and have received moreand more attention. However, unlike raster images which can be easily obtained,acquiring high-quality vector graphics, typically through automaticallyconverting from raster images remains a significant challenge, especially formore complex images such as photos or artworks. In this paper, we proposeSAMVG, a multi-stage model to vectorize raster images into SVG (Scalable VectorGraphics). Firstly, SAMVG uses general image segmentation provided by theSegment-Anything Model and uses a novel filtering method to identify the bestdense segmentation map for the entire image. Secondly, SAMVG then identifiesmissing components and adds more detailed components to the SVG. Through aseries of extensive experiments, we demonstrate that SAMVG can produce highquality SVGs in any domain while requiring less computation time and complexitycompared to previous state-of-the-art methods.

SAMVG: A Multi-stage Image Vectorization Model with the Segment-Anything Model