Urban-GS：面向紧凑高保真航拍到街景重建的统一3D高斯泼溅框架
Urban-GS: A Unified 3D Gaussian Splatting Framework for Compact and High-Fidelity Aerial-to-Street Reconstruction
针对航拍与街景视角差异极大的联合重建场景，提出自适应稠密化与基于贡献的锚框剪枝，可在保持渲染质量的同时将存储降低41%，为大规模城市级3DGS重建提供了可复用的紧凑化方案。
原文依据 we first develop an Aerial-Street Joint Adaptive Densification method to resolve the densification conflicts arising from large view coverage disparity
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论近期，3D高斯泼溅（3DGS）革新了辐射场重建，实现了高效且高保真的新视角合成。然而，将航拍与街景图像无缝集成以建模城市场景，对3DGS而言仍是一项重大挑战。这种联合设置面临极端的视角覆盖差异、复杂的多尺度细节以及不平衡的视点分布。在本工作中，我们提出Urban-GS，一个基于高斯泼溅的新颖框架，用于从航拍和街景两种视角对城市场景进行紧凑的统一重建与高保真渲染。具体而言，我们首先开发了一种航拍-街景联合自适应稠密化方法，以解决由大视角覆盖差异引起的稠密化冲突。接着，我们引入了一种基于贡献的锚框剪枝策略，以有效缓解捕捉多尺度场景细节所带来的存储开销。此外，我们提出了一种全局到局部的优化策略，以细化因视点分布不平衡而优化不足的区域的重建。在不同城市场景数据集上的实验表明，Urban-GS在新视角渲染质量上显著优于最优方法，同时平均降低41%的存储开销。
英文原文摘要 1326 字符
Recently, 3D Gaussian Splatting (3DGS) has revolutionized radiance field reconstruction, enabling efficient and high-fidelity novel view synthesis. However, seamless integration of both aerial and street view images to model urban scenes remains a significant challenge for 3DGS. This joint setting suffers from extreme view coverage disparity, complex multi-scale details, and imbalanced viewpoint distributions.In this work, we present Urban-GS, a novel framework built upon Gaussian Splatting for the compact unified reconstruction and high-fidelity rendering of urban scenes from both aerial and street views. Specifically, we first develop an Aerial-Street Joint Adaptive Densification method to resolve the densification conflicts arising from large view coverage disparity. We then introduce a Contribution-based Anchor Pruning strategy to effectively mitigate the storage overhead from capturing multi-scale scene details. Furthermore, we propose a Global-to-Local Optimization strategy to refine the reconstruction of under-optimized regions resulting from imbalanced view distributions. Experiments across diverse urban scene datasets demonstrate that Urban-GS significantly outperforms the state-of-the-art method in novel-view rendering quality, while simultaneously reducing storage overhead by an average of 41%.