HUG通过分层高斯表示提升大场景航拍图的渲染质量与效率。
HUG: Hierarchical Urban Gaussian Splatting with Block-Based Reconstruction for Large-Scale Aerial Scenes
- 基于可见性分块，快速高效划分大规模数据
- 分层加权训练显著改善重建质量，多数据集达顶尖水平
- 适合需要实时渲染的大规模城市三维重建应用
3DGS 是新兴且日益流行的新型视图合成技术，其高度逼真的渲染效果和实时渲染能力使其在诸多领域具有广阔前景。然而，在大规模航拍城市场景中，现有3DGS方法面临内存占用过高、训练速度慢、分割耗时长以及因数据量增大导致的渲染质量显著下降等问题。为此，我们提出 extbf{HUG}，一种利用分层神经高斯表示增强数据分割与重建质量的新方法。首先，设计了一种基于可见性的数据分块方法，简单高效，显著优于现有方法的处理速度；其次，引入新颖的分层加权训练策略，并结合其他优化手段，大幅提升了重建质量。该方法在1个合成数据集和4个真实世界数据集上均达到当前最优性能。
原文摘要 · Abstract (English)
3DGS is an emerging and increasingly popular technology in the field of novel view synthesis. Its highly realistic rendering quality and real-time rendering capabilities make it promising for various applications. However, when applied to large-scale aerial urban scenes, 3DGS methods suffer from issues such as excessive memory consumption, slow training times, prolonged partitioning processes, and significant degradation in rendering quality due to the increased data volume. To tackle these challenges, we introduce \textbf{HUG}, a novel approach that enhances data partitioning and reconstruction quality by leveraging a hierarchical neural Gaussian representation. We first propose a visibility-based data partitioning method that is simple yet highly efficient, significantly outperforming existing methods in speed. Then, we introduce a novel hierarchical weighted training approach, combined with other optimization strategies, to substantially improve reconstruction quality. Our method achieves state-of-the-art results on one synthetic dataset and four real-world datasets.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。