Paper Overview
Field: Computer Vision (CV) Authors: Weijian Chen, Weibo Yao, Yuhang Zhang Published: 2026-07-10 arXiv: 2507.08181
Summary
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:
- Global coarse stage: leverages sky-sphere modeling and panoramic monocular depth supervision for reliable geometry.
- Refinement stage: G²PS builds adaptive bounding volumes via disparity-driven uncertainty and assigns cameras using gradient-based importance scoring.
- Paper: https://arxiv.org/abs/2507.08181
Additionally, the authors construct Pano360, the first large-scale panoramic outdoor scene reconstruction benchmark dataset. Extensive experiments demonstrate that G²PS achieves state-of-the-art rendering quality while maintaining scalable block-parallel training.
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