City-Mesh3R：面向仿真就绪的城市级多视图三维网格重建
City-Mesh3R: Simulation-Ready City-Scale 3D Mesh Reconstruction from Multi-View Images
AI总结：
提出City-Mesh3R框架，通过分治策略从大规模无序图像集合端到端重建水密表面网格，解决城市尺度重建中几何不完整、表面不规则及计算复杂性问题。
摘要：
从多视图图像进行城市级三维表面重建以支持下游三维仿真，由于城市场景的规模和复杂性，带来了极具挑战性的问题。现有的基于NeRF、高斯泼溅等方法的城市级三维重建技术，常因几何不完整/缺失以及不规则、噪声表面而无法恢复可用于仿真的三维网格。将现有小规模三维重建方法扩展到任意大规模城市场景因计算复杂而不可行。我们提出City-Mesh3R，一个可扩展的框架，直接从大规模无序图像集合重建水密表面网格。与近期使用全局稀疏SfM点云初始化后分布式稠密重建大规模场景的方法不同，我们的方法采用分治策略，遵循端到端的图像到网格三维重建流程。通过拓扑图像聚类、聚类独立稀疏SfM和地图合并重建稀疏城市地图，无需穷举图像特征匹配。然后对该地图进行空间划分，执行几何感知的相机选择，接着进行稠密表面重建，并使用曲率感知的自适应顶点密度重网格化进行表面细化。这些分区网格随后拼接成城市全局网格。所提出的端到端框架在城市级重建数据集上进行了评估。定性和定量结果表明，我们的方法能生成具有规则几何、捕捉精细表面细节的高保真水密三维网格，并因其分布式端到端处理而适用于任意大规模场景。
英文摘要：
City-scale 3D surface reconstruction from multiview images for downstream 3D simulation, poses highly challenging problems due to the scale and complexity of urban scenes. Existing city-scale 3D reconstruction methods based on NeRF, Gaussian Splatting etc. often fail to recover 3D meshes ready for simulation due to incomplete/missing geometry and irregular, noisy surfaces. Scaling existing small-scale 3D reconstruction methods to arbitrarily large urban scenes is highly infeasible due to their computational complexity. We present City-Mesh3R, a scalable framework for reconstructing watertight surface meshes directly from large unordered image collections. Unlike recent methods which use global sparse SfM point-cloud initialization followed by a distributed 3D dense reconstruction of large-scale scenes, our method follows an end-to-end images-to-mesh 3D reconstruction approach using a divide-and-conquer strategy. The sparse city map is reconstructed via topological image clustering, cluster-wise independent sparse SfM and map merging, without need for exhaustive image feature matching. Then this map is partitioned spatially to perform geometry-aware camera selection, followed by dense surface reconstruction and surface refinement using curvature-aware adaptive vertex density remeshing. These partition meshes are then stitched together to produce the global mesh of the city. The proposed end-to-end framework is evaluated on city-scale reconstruction datasets. As demonstrated by our qualitative and quantitative results, our proposed method yields high-fidelity watertight 3D meshes with regular geometry, capturing fine surface details, and is suitable for scaling to arbitrarily large scenes owing to the end-to-end processing in a distributed setting.
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