PanoLOG: Geometry and Gradient-based Partitioning for Panoramic Outdoor 3DGS Reconstruction

Forum topic · 小凯 · 2026-07-11

Summary

This post introduces PanoLOG (arXiv:2507.08181), a two-stage coarse-to-fine framework for scaling 3D Gaussian Splatting (3DGS) to large outdoor scenes using panoramic images with equirectangular projection (ERP). Panoramic capture reduces data collection effort thanks to a full 360-degree field of view, but the resulting omnipresent visibility breaks existing partitioning strategies that rely on local camera frustums, causing block-wise optimization to degrade into global training. PanoLOG addresses this with a Geometry and Gradient-based Partitioning Strategy (G2PS). In the global coarse stage, it uses sky-sphere modeling and panoramic monocular depth supervision for reliable geometry; in the refinement stage, G2PS constructs adaptive bounding volumes from disparity-driven uncertainty and assigns cameras via gradient-based importance scoring. The authors also release Pano360, the first large-scale panoramic outdoor scene reconstruction benchmark dataset. Experiments show G2PS achieves state-of-the-art rendering quality while maintaining scalable block-parallel training. By Weijian Chen, Weibo Yao, and Yuhang Zhang.

Paper Overview

Field: Computer Vision Authors: Weijian Chen, Weibo Yao, Yuhang Zhang arXiv: 2507.08181

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° 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, the authors propose PanoLOG, a two-stage coarse-to-fine framework equipped with a Geometry and Gradient-based Partitioning Strategy (G²PS) tailored for large-scale panoramic 3DGS reconstruction.

Key Components

Global coarse stage: sky-sphere modeling combined with panoramic monocular depth supervision provides reliable geometry.
Refinement stage (G²PS): builds adaptive bounding volumes via disparity-driven uncertainty, and assigns cameras to blocks using gradient-based importance scoring.
Pano360 benchmark: the first large-scale panoramic outdoor scene reconstruction dataset, introduced alongside the method.

Results

Extensive experiments demonstrate that G²PS achieves state-of-the-art rendering quality while preserving scalable block-parallel training.