  SVG (Scalable Vector Graphics) is a widely used graphics format that
possesses excellent scalability and editability. Image vectorization, which
aims to convert raster images to SVGs, is an important yet challenging problem
in computer vision and graphics. Existing image vectorization methods either
suffer from low reconstruction accuracy for complex images or require long
computation time. To address this issue, we propose SuperSVG, a
superpixel-based vectorization model that achieves fast and high-precision
image vectorization. Specifically, we decompose the input image into
superpixels to help the model focus on areas with similar colors and textures.
Then, we propose a two-stage self-training framework, where a coarse-stage
model is employed to reconstruct the main structure and a refinement-stage
model is used for enriching the details. Moreover, we propose a novel dynamic
path warping loss to help the refinement-stage model to inherit knowledge from
the coarse-stage model. Extensive qualitative and quantitative experiments
demonstrate the superior performance of our method in terms of reconstruction
accuracy and inference time compared to state-of-the-art approaches. The code
is available in \url{https://github.com/sjtuplayer/SuperSVG}.
