PanoLOG: Geometry and Gradient-based Partitioning for Panoramic Outdoor 3D Reconstruction (arXiv 2607.08769)

Forum topic · 小凯 · 2026-07-12

Summary

A paper presented on zhichai.net introduces PanoLOG, 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 acquisition cost thanks to its 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 degenerate 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 boundary volumes via disparity-driven uncertainty and assigns cameras through gradient-based importance scoring. The authors also release Pano360, the first large-scale panoramic dataset benchmark for outdoor scene reconstruction. Experiments show state-of-the-art rendering quality while maintaining scalable block-parallel training. The arXiv ID is 2607.08769, with models, training code, and the dataset publicly released.

Paper Overview

Research field: Computer Vision (CV) Authors: Weijian Chen, Weibo Yao, Yuhang Zhang, Xiaolin Tang, Guo Wang, Weijun Zhang, Xitong Gao, Yihao Chen, Hongde Qin, Lu Qi Published: 2026-07-09 arXiv: 2607.08769

Summary (Chinese translation)

Scaling 3D Gaussian Splatting (3DGS) to large outdoor scenes is costly in both data acquisition and computation. 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. The authors therefore 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.

Global coarse stage: leverages sky-sphere modeling and panoramic monocular depth supervision to obtain reliable geometry.
Refinement stage: G²PS builds adaptive boundary volumes via disparity-driven uncertainty and assigns cameras through gradient-based importance scoring.
Pano360 benchmark: the first large-scale panoramic dataset benchmark for outdoor scene reconstruction.
Results: extensive experiments show G²PS achieves state-of-the-art rendering quality while preserving scalable block-parallel training.
Models, training code, and the dataset are publicly released.

Original Abstract (excerpt)

> 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 a...

Full paper: arXiv:2607.08769

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