针对全景图编辑的几何畸变问题，提出基于球面几何的新框架
SphereDrag: Spherical Geometry-Aware Panoramic Image Editing
- 利用球面旋转自适应处理边界断裂问题
- 通过大圆轨迹追踪提升移动路径精度，改善形变
- 按球面位置动态调整搜索范围，解决像素密度不均
图像编辑在平面图像上已取得显著进展，但全景图像编辑仍处于探索阶段。由于其球面几何结构和投影畸变，全景图像面临三个关键挑战：边界不连续、轨迹形变和像素密度不均。为此，我们提出SphereDrag，一种利用球面几何知识实现精准可控全景编辑的新框架。具体而言，自适应重投影（AR）通过自适应球面旋转解决不连续问题；大圆轨迹调整（GCTA）更准确地追踪运动轨迹；球面搜索区域追踪（SSRT）根据球面位置自适应调整搜索范围，以应对像素密度不均。此外，我们构建了PanoBench，一个包含多对象、多风格复杂编辑任务的全景编辑基准数据集，提供标准化评估体系。实验表明，相比现有方法，SphereDrag在几何一致性与图像质量上均有显著提升，最高达10.5%相对改进。
原文摘要 · Abstract (English)
Image editing has made great progress on planar images, but panoramic image editing remains underexplored. Due to their spherical geometry and projection distortions, panoramic images present three key challenges: boundary discontinuity, trajectory deformation, and uneven pixel density. To tackle these issues, we propose SphereDrag, a novel panoramic editing framework utilizing spherical geometry knowledge for accurate and controllable editing. Specifically, adaptive reprojection (AR) uses adaptive spherical rotation to deal with discontinuity; great-circle trajectory adjustment (GCTA) tracks the movement trajectory more accurate; spherical search region tracking (SSRT) adaptively scales the search range based on spherical location to address uneven pixel density. Also, we construct PanoBench, a panoramic editing benchmark, including complex editing tasks involving multiple objects and diverse styles, which provides a standardized evaluation framework. Experiments show that SphereDrag gains a considerable improvement compared with existing methods in geometric consistency and image quality, achieving up to 10.5% relative improvement.
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