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

翻译：基于学习的图像拼接技术通常涉及三个不同的阶段：配准、融合和矩形化。这些阶段通常依次执行，各自独立训练，导致潜在的级联错误传播和复杂的参数调优问题。在重新思考融合和矩形化阶段的数学建模时，我们发现这些过程可以有效地合并为一个单一的多变性强度修复问题。因此，我们提出了简单鲁棒拼接器（SRStitcher），这是一种无需训练的高效图像拼接方法，将融合和矩形化阶段合并为一个统一模型。通过采用加权掩码和大规模生成模型，SRStitcher可以在单次推理中解决融合和矩形化问题，无需额外训练或微调其他模型。我们的方法不仅简化了拼接流程，还增强了对配准错误的容错能力。大量实验表明，SRStitcher在定量评估和定性评价中均优于最先进的（SOTA）方法。代码发布在https://github.com/yayoyo66/SRStitcher