解决全景图3D重建中块状优化失效问题，实现高效大规模户外场景重建。
Geometry and Gradient-based Partitioning for Panoramic Outdoor Reconstruction
- 基于几何与梯度的自适应分块策略，避免全局优化
- 在Pano360数据集上实现高质量渲染与可扩展训练
- 适合大规模全景3D重建研究者使用
将3D高斯溅射（3DGS）扩展至大规模户外场景在数据采集和计算上成本高昂。采用等距圆柱投影（ERP）的全景图像可凭借360°视场减少采集工作量，但其全向可见性破坏了依赖局部相机视锥的传统分块策略，导致块状优化退化为全局训练。为此，我们提出PanoLOG，一种从粗到精的两阶段框架，配备专为大规模全景3DGS重建设计的几何与梯度分块策略。在全局粗略阶段，PanoLOG利用天空球建模和全景单目深度监督获取可靠几何信息；在细化阶段，G²PS通过视差驱动的不确定性构建自适应包围体，并基于梯度重要性评分分配相机。此外，我们构建了首个大规模全景户外场景重建基准数据集Pano360。大量实验表明，G²PS在保持可扩展块并行训练的同时达到最优渲染质量。我们的模型、训练代码和数据集均已公开。
原文摘要 · Abstract (English)
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.
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