3D Gaussian Splatting (3DGS) has shown promising performance in novel view synthesis. Previous methods adapt it to obtaining surfaces of either individual 3D objects or within limited scenes. In this paper, we make the first attempt to tackle the challenging task of large-scale scene surface reconstruction. This task is particularly difficult due to the high GPU memory consumption, different levels of details for geometric representation, and noticeable inconsistencies in appearance. To this end, we propose GigaGS, the first work for high-quality surface reconstruction for large-scale scenes using 3DGS. GigaGS first applies a partitioning strategy based on the mutual visibility of spatial regions, which effectively grouping cameras for parallel processing. To enhance the quality of the surface, we also propose novel multi-view photometric and geometric consistency constraints based on Level-of-Detail representation. In doing so, our method can reconstruct detailed surface structures. Comprehensive experiments are conducted on various datasets. The consistent improvement demonstrates the superiority of GigaGS.
3D Gaussian Splatting (3DGS) 在新视图合成中展现了出色的性能。之前的方法主要应用于单个 3D 物体或有限场景的表面获取。在本文中，我们首次尝试解决大规模场景表面重建这一具有挑战性的任务。由于高GPU内存消耗、几何表示的不同细节层次以及外观上的显著不一致性，这一任务尤为困难。为此，我们提出了 GigaGS，这是第一个基于 3DGS 的高质量大规模场景表面重建方法。GigaGS 首先应用了一种基于空间区域相互可见性的分割策略，有效地将摄像机进行分组，以便并行处理。为了提升表面质量，我们还提出了基于多层细节（Level-of-Detail）表示的多视图光度和几何一致性约束。通过这样做，我们的方法能够重建出精细的表面结构。我们在多个数据集上进行了全面的实验，实验结果显示了 GigaGS 的显著优势。