PanoLOG: Geometry and Gradient-based Partitioning for Panoramic Outdoor Reconstruction

Scaling 3D Gaussian Splatting (3DGS) to large outdoor scenes is costly in both data acquisition and computation. Adopting panoramic images with equirectangular projection (ERP) can reduce capture effort via their full 360° field of view, yet the resulting omnipresent visibility invalidates existing partitioning strategies that rely on local camera frustums, causing block-wise optimization to degenerate into global training. Thus, we propose PanoLOG (Panoramic Large-scale Outdoor Gaussian Splatting), a two-stage coarse-to-fine framework equipped with a Geometry and Gradient-based Partitioning Strategy (G²PS) tailored for large-scale panoramic 3DGS reconstruction. In the global coarse stage, PanoLOG leverages sky-sphere modeling and panoramic monocular depth supervision for reliable geometry, while in the refinement stage, G²PS builds adaptive bounding volumes via parallax-driven uncertainty and assigns cameras via gradient-based importance scoring. Furthermore, we construct Pano360, the first large-scale panoramic benchmark for outdoor scene reconstruction. Extensive experiments demonstrate that PanoLOG achieves state-of-the-art rendering quality while maintaining scalable, block-parallel training. Our models, training code, and dataset will be publicly available.