城市级3D重建终于有了一个能直接用于仿真的方案——City-Mesh3R 解决了网格不完整和表面噪声的痛点，做自动驾驶仿真或数字孪生的团队值得关注。
City-Mesh3R 提出了一种可扩展的框架，能从大规模无序图像集合中直接重建水密表面网格，解决了现有方法因几何不完整、表面不规则而无法用于仿真的问题。该方法采用分治策略：先通过拓扑图像聚类和稀疏SfM构建城市地图，再空间分区进行几何感知相机选择和稠密重建，最后拼接成全局网格。与传统依赖全局SfM点云初始化的方法不同，City-Mesh3R 实现了端到端的图像到网格重建，计算复杂度更低。实验表明，该方法能生成高保真、几何规则的水密网格，捕捉精细表面细节，且支持任意大规模场景的分布式处理。
City-Mesh3R: Simulation-Ready City-Scale 3D Mesh Reconstruction from Multi-View Images
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.