CurveSDF: Binary Image Vectorization Using Signed Distance Fields

Binary image vectorization is a classical and fundamental problem in the areas of Computer Graphics and Computer Vision. Existing image vectorization methods are mainly based on global optimization, typically failing to preserve important details on outlines due to the incapability of learning high-level knowledge from training data. To address this problem, we propose CurveSDF to facilitate the learning of vectorization for 2D outlines. Our method consists of the following three modules: convex separation, signed distance field (SDF) generation, and curve intersection calculation. Specifically, we first divide an input binary image into convex elements. Then, we use restrained curve-hyperplane divisions to generate their SDFs and precisely reconstruct the original image. Finally, we convert the generated SDFs to vector outlines composed of both Bezier curves and line segments. Moreover, our method is self-constrained, and thus there is no need to use any vector data for training. Experimental results demonstrate the effectiveness of our method and its superiority against other existing approaches for binary image vectorization.