Seamlessly Natural: Image Stitching with Natural Appearance Preservation

Conventional image stitching pipelines predominantly rely on homographic alignment, whose planar assumption often breaks down in dual-camera configurations capturing non-planar scenes, leading to visual artifacts such as geometric warping, spherical bulging, and structural deformation. To address these limitations, this paper presents SENA (SEamlessly NAtural), a geometry-driven image stitching approach with three complementary contributions. First, we propose a hierarchical affine-based warping strategy that combines global affine initialization, local affine refinement, and a smooth free-form deformation field regulated by seamguard adaptive smoothing. This multi-scale design preserves local shape, parallelism, and aspect ratios, thereby reducing the hallucinated distortions commonly associated with homography-based models. Second, SENA incorporates 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 or depth estimation. Third, building upon this adequate zone, we apply anchor-based seamline cutting and segmentation, enforcing one-to-one geometric correspondence between image pairs by construction and reducing ghosting, duplication, and smearing artifacts. Extensive experiments on challenging datasets demonstrate that SENA achieves alignment accuracy comparable to leading homography-based methods, while providing improved shape preservation, texture continuity, and overall visual realism.