Image-based 3D modeling is an effective method for reconstructing large-scale scenes, especially city-level scenarios. In the image-based modeling pipeline, obtaining a watertight mesh model from a noisy multi-view stereo point cloud is a key step toward ensuring model quality. However, some state-of-the-art methods rely on the global Delaunay-based optimization formed by all the points and cameras; thus, they encounter scaling problems when dealing with large scenes. To circumvent these limitations, this study proposes a scalable point-cloud meshing approach to aid the reconstruction of city-scale scenes with minimal time consumption and memory usage. Firstly, the entire scene is divided along the x and y axes into several overlapping chunks so that each chunk can satisfy the memory limit. Then, the Delaunay-based optimization is performed to extract meshes for each chunk in parallel. Finally, the local meshes are merged together by resolving local inconsistencies in the overlapping areas between the chunks. We test the proposed method on three city-scale scenes with hundreds of millions of points and thousands of images, and demonstrate its scalability, accuracy, and completeness, compared with the state-of-the-art methods.

中文翻译：

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可扩展的点云网格划分，用于基于图像的大规模3D建模

基于图像的3D建模是重构大型场景（尤其是城市级场景）的有效方法。在基于图像的建模管道中，从嘈杂的多视图立体点云中获取水密网格模型是确保模型质量的关键一步。但是，某些最新方法依赖于所有点和摄像机形成的基于Delaunay的全局优化。因此，它们在处理大型场景时会遇到缩放问题。为了克服这些限制，本研究提出了一种可扩展的点云网格划分方法，以最少的时间消耗和内存使用量来帮助重建城市规模的场景。首先，将整个场景沿x和y轴分为几个重叠的块，以便每个块都可以满足内存限制。然后，执行基于Delaunay的优化，以并行提取每个块的网格。最后，通过解决块之间重叠区域中的局部不一致性，将局部网格合并在一起。我们在具有数亿个点和数以千计的图像的三个城市规模的场景上测试了该方法，并与最新方法进行了比较，证明了其可扩展性，准确性和完整性。