Abstract
This paper introduces SENA (SEamlessly NAtural), a geometry-driven image stitching approach that prioritizes structural fidelity in challenging real-world scenes characterized by parallax and depth variation. Conventional image stitching relies on homographic alignment, but this rigid planar assumption often fails in dual-camera setups with significant scene depth, leading to distortions such as visible warps and spherical bulging. SENA addresses these fundamental limitations through three key contributions. First, we propose a hierarchical affine-based warping strategy, combining global affine initialization with local affine refinement and smooth free-form deformation. This design preserves local shape, parallelism, and aspect ratios, thereby avoiding the hallucinated structural distortions commonly introduced by homography-based models. Second, we introduce a geometry-driven adequate zone detection mechanism that identifies parallax-minimized regions directly from the disparity consistency of RANSAC-filtered feature correspondences, without relying on semantic segmentation. Third, building upon this adequate zone, we perform anchor-based seamline cutting and segmentation, enforcing a one-to-one geometric correspondence across image pairs by construction, which effectively eliminates ghosting, duplication, and smearing artifacts in the final panorama. Extensive experiments conducted on challenging datasets demonstrate that SENA achieves alignment accuracy comparable to leading homography-based methods, while significantly outperforming them in critical visual metrics such as shape preservation, texture integrity, and overall visual realism.
Abstract (translated)
本文介绍了SENA（SEamlessly NAtural），这是一种以几何为基础的图像拼接方法，优先考虑具有视差和深度变化等挑战性现实场景中的结构保真度。传统的图像拼接依赖于射影对齐，但这种刚性的平面假设在双摄像头设置中面对显著场景深度时往往失效，导致可见扭曲和球面膨胀等问题。SENA通过三项关键贡献解决了这些根本限制。 首先，我们提出了一种基于层次仿射变换的图像变形策略，结合全局仿射初始化与局部仿射细化以及平滑自由形式变形。这种设计保留了局部形状、平行性和纵横比，从而避免了由射影模型引入的结构扭曲问题。 其次，我们引入了一种几何驱动的充分区域检测机制，该机制通过计算RANSAC滤波特征对应关系的一致性差异直接识别出视差最小化的区域，并不需要依赖语义分割技术。 第三，在此基础上，我们基于锚点进行缝线切割和分段处理，强制确保每对图像之间存在一对一的几何对应关系，从而有效地消除了最终全景图中的幽灵、重复和模糊等伪影。 通过在具有挑战性的数据集上进行的大量实验表明，SENA实现了与领先的射影模型方法相媲美的对齐精度，但在诸如形状保持、纹理完整性和整体视觉真实感等关键视觉指标方面表现显著更好。
URL
https://arxiv.org/abs/2601.01257
