Streamlining the Image Stitching Pipeline: Integrating Fusion and Rectangling into a Unified Model

Learning-based image stitching techniques typically involve three distinct stages: registration, fusion, and rectangling. These stages are often performed sequentially, each trained independently, leading to potential cascading error propagation and complex parameter tuning challenges. In rethinking the mathematical modeling of the fusion and rectangling stages, we discovered that these processes can be effectively combined into a single, variety-intensity inpainting problem. Therefore, we propose the Simple and Robust Stitcher (SRStitcher), an efficient training-free image stitching method that merges the fusion and rectangling stages into a unified model. By employing the weighted mask and large-scale generative model, SRStitcher can solve the fusion and rectangling problems in a single inference, without additional training or fine-tuning of other models. Our method not only simplifies the stitching pipeline but also enhances fault tolerance towards misregistration errors. Extensive experiments demonstrate that SRStitcher outperforms state-of-the-art (SOTA) methods in both quantitative assessments and qualitative evaluations. The code is released at https://github.com/yayoyo66/SRStitcher