Abstract
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
Abstract (translated)
基于学习的图像拼接技术通常包括三个不同的阶段：注册、融合和矩形化。这些阶段通常按顺序执行，每个阶段都经过独立训练，这可能导致级联错误传播和复杂参数调整挑战。在重新考虑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合并成一个单一的多样性强度在补救问题中。因此，我们提出了简单且鲁棒的全拼接器（SRStitcher）高效的无训练图像拼接方法，将融合和矩形化阶段合并为一个统一的模型。通过采用加权掩码和大规模生成模型，SRStitcher可以在单个推理中解决融合和矩形化问题，而无需其他模型的微调或训练。我们的方法不仅简化了拼接流程，还提高了对配准错误容错的能力。大量实验证明，SRStitcher在定量评估和定性评估方面都优于最先进的（SOTA）方法。代码发布在https://这一URL
URL
https://arxiv.org/abs/2404.14951
