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 this https URL

通过将融合和矩形化阶段结合成一个复合模型，我们提出了一种简单和鲁棒的无需训练的图像拼接方法（SRStitcher），该方法不仅简化了图像拼接流程，而且增强了对误注册错误的容错能力。