解决大规模航拍场景下高精度表面重建难题，提升城市复杂环境重建质量。
ULSR-GS: Ultra Large-scale Surface Reconstruction Gaussian Splatting with Multi-View Geometric Consistency
- 分区域选图+多视角最优匹配，精准选择训练图像。
- 基于多视角几何一致性增强点云密度，细节更丰富。
- 在超大规模航拍数据上显著优于现有方法，适合城市级重建。
尽管高斯点阵（Gaussian Splatting, GS）在高效渲染与小范围表面提取方面表现优异，但在处理大规模航拍场景表面重建任务时仍存在不足。为此，本文提出ULSR-GS框架，专为超大规模场景的高保真表面重建设计，克服了现有基于GS的网格提取方法的局限性。具体而言，我们采用点到图像的分区策略，并结合多视角最优视图匹配原则，为每个子区域选取最佳训练图像。此外，在训练过程中，ULSR-GS引入基于多视角几何一致性的密集化策略，以增强表面细节。实验结果表明，ULSR-GS在大型航拍摄影测量基准数据集上超越其他先进GS方法，在复杂城市环境中显著提升了表面提取精度。项目页面：https://ulsrgs.github.io。
原文摘要 · Abstract (English)
While Gaussian Splatting (GS) demonstrates efficient and high-quality scene rendering and small area surface extraction ability, it falls short in handling large-scale aerial image surface extraction tasks. To overcome this, we present ULSR-GS, a framework dedicated to high-fidelity surface extraction in ultra-large-scale scenes, addressing the limitations of existing GS-based mesh extraction methods. Specifically, we propose a point-to-photo partitioning approach combined with a multi-view optimal view matching principle to select the best training images for each sub-region. Additionally, during training, ULSR-GS employs a densification strategy based on multi-view geometric consistency to enhance surface extraction details. Experimental results demonstrate that ULSR-GS outperforms other state-of-the-art GS-based works on large-scale aerial photogrammetry benchmark datasets, significantly improving surface extraction accuracy in complex urban environments. Project page: https://ulsrgs.github.io.
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