Geometry and Gradient-based Partitioning for Panoramic Outdoor Reconstruction

2026-07-09

AI Summary

This work addresses the challenges of high data and computational costs, as well as the failure of conventional frustum-based partitioning strategies, in 3D Gaussian Splatting (3DGS) reconstruction of large-scale outdoor panoramic scenes. The authors propose PanoLOG, a coarse-to-fine two-stage adaptive tiling framework that introduces a novel geometry- and gradient-driven partitioning strategy (G²PS). This approach integrates celestial sphere modeling, monocular depth supervision, disparity-driven uncertainty estimation, and gradient-based importance scoring to enable efficient voxelization and camera assignment. The study also presents Pano360, the first large-scale outdoor panoramic 3DGS benchmark dataset, achieving state-of-the-art rendering quality while maintaining parallel scalability. All code, models, and datasets are publicly released.

Abstract

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 $360^{\circ}$ 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, a two-stage coarse-to-fine framework equipped with a Geometry and Gradient-based Partitioning Strategy 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, G$^2$PS builds adaptive bounding volumes via parallax-driven uncertainty and assigns cameras via gradient-based importance scoring. Furthermore, we construct Pano360, the first benchmark on large-scale panoramic dataset for outdoor scene reconstruction. Extensive experiments demonstrate that G$^2$PS achieves state-of-the-art rendering quality while maintaining scalable, block-parallel training. Our models, training code, and dataset are publicly available.

Problem

• 3D Gaussian Splatting
• panoramic reconstruction
• scene partitioning
• large-scale outdoor scenes
• equirectangular projection

Innovation

• 3D Gaussian Splatting
• panoramic reconstruction
• geometry and gradient-based partitioning
• block-parallel training
• equirectangular projection