Omnidirectional images are increasingly used in robotics and vision due to their wide field of view. However, extending 3D Gaussian Splatting (3DGS) to panoramic camera models remains challenging, as existing formulations are designed for perspective projections and naive adaptations often introduce distortion and geometric inconsistencies. We present Spherical-GOF, an omnidirectional Gaussian rendering framework built upon Gaussian Opacity Fields (GOF). Unlike projection-based rasterization, Spherical-GOF performs GOF ray sampling directly on the unit sphere in spherical ray space, enabling consistent ray-Gaussian interactions for panoramic rendering. To make the spherical ray casting efficient and robust, we derive a conservative spherical bounding rule for fast ray-Gaussian culling and introduce a spherical filtering scheme that adapts Gaussian footprints to distortion-varying panoramic pixel sampling. Extensive experiments on standard panoramic benchmarks (OmniBlender and OmniPhotos) demonstrate competitive photometric quality and substantially improved geometric consistency. Compared with the strongest baseline, Spherical-GOF reduces depth reprojection error by 57% and improves cycle inlier ratio by 21%. Qualitative results show cleaner depth and more coherent normal maps, with strong robustness to global panorama rotations. We further validate generalization on OmniRob, a real-world robotic omnidirectional dataset introduced in this work, featuring UAV and quadruped platforms. The source code and the OmniRob dataset will be released at https://github.com/1170632760/Spherical-GOF.

翻译：全景图像因其广阔的视野范围在机器人与视觉领域应用日益广泛。然而，将三维高斯泼溅（3DGS）扩展至全景相机模型仍具挑战性，现有方法专为透视投影设计，简单适配常导致畸变与几何不一致。本文提出球面高斯透明度场（Spherical-GOF），一种基于高斯透明度场（GOF）的全方位高斯渲染框架。区别于基于投影的光栅化方法，Spherical-GOF 在球面射线空间的单位球面上直接执行 GOF 射线采样，实现了全景渲染中射线-高斯相互作用的几何一致性。为使球面射线投射高效鲁棒，我们推导了用于快速射线-高斯剔除的保守球面边界规则，并提出了自适应高斯足迹至畸变变化全景像素采样的球面滤波方案。在标准全景基准数据集（OmniBlender 与 OmniPhotos）上的大量实验表明，本方法在光度质量上具有竞争力，且几何一致性显著提升。相较于最强基线模型，Spherical-GOF 将深度重投影误差降低 57%，循环内点比率提升 21%。定性结果显示更清晰的深度图与更连贯的法向图，并对全局全景旋转具有强鲁棒性。我们进一步在 OmniRob（本研究提出的真实世界机器人全景数据集，包含无人机与四足机器人平台）上验证了泛化性能。源代码与 OmniRob 数据集将于 https://github.com/1170632760/Spherical-GOF 发布。