City-Mesh3R: Simulation-Ready City-Scale 3D Mesh Reconstruction from Multi-View Images

• URL: http://arxiv.org/abs/2605.30310v1
• Date: Thu, 28 May 2026 17:53:26 GMT
• Title: City-Mesh3R: Simulation-Ready City-Scale 3D Mesh Reconstruction from Multi-View Images
• Abstract summary: City-Mesh3R is a scalable framework for reconstructing watertight surface meshes directly from large unordered image collections.<n>The proposed end-to-end framework is evaluated on city-scale reconstruction datasets.
• Score: 10.051585377704464
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

Related papers

• City-Level 3D Surface Reconstruction with Viewpoint Orientation Partitioning and Scene Completion [92.92843111081761]
  Multi-view 3D surface reconstruction is a longstanding challenge in computer vision.<n>Recent large-scale reconstruction methods based on 3D Gaussian Splatting (3DGS) achieve impressive novel-view synthesis.<n>We propose a novel yet simple partitioning method to efficiently and faithfully reconstruct large-scale scene surfaces.
  arXiv  Detail & Related papers  (2026-07-04T08:44:12Z)
• SurfaceSplat: Connecting Surface Reconstruction and Gaussian Splatting [92.13737830605902]
  Surface reconstruction and novel view rendering from sparse-view images are challenging.<n>We propose a novel hybrid method that combines the strengths of both approaches.<n>Our method surpasses state-of-the-art approaches in surface reconstruction and novel view synthesis on the DTU and MobileBrick datasets.
  arXiv  Detail & Related papers  (2025-07-21T13:25:03Z)
• Stereo-GS: Multi-View Stereo Vision Model for Generalizable 3D Gaussian Splatting Reconstruction [30.518107360632488]
  Generalizable 3D Gaussian Splatting reconstruction showcases advanced Image-to-3D content creation.<n>method provides an efficient, scalable solution for real-world 3D content generation.
  arXiv  Detail & Related papers  (2025-07-20T11:33:13Z)
• DiMeR: Disentangled Mesh Reconstruction Model [29.827345186012558]
  DiMeR is a novel geometry-texture disentangled feed-forward model with 3D supervision for sparse-view mesh reconstruction.<n>We streamline the algorithm of mesh extraction by eliminating modules with low performance/cost ratios and redesigning regularization losses with 3D supervision.<n>Extensive experiments demonstrate that DiMeR generalises across sparse-view-, single-image-, and text-to-3D tasks, consistently outperforming baselines.
  arXiv  Detail & Related papers  (2025-04-24T15:39:20Z)
• HORT: Monocular Hand-held Objects Reconstruction with Transformers [61.36376511119355]
  Reconstructing hand-held objects in 3D from monocular images is a significant challenge in computer vision.<n>We propose a transformer-based model to efficiently reconstruct dense 3D point clouds of hand-held objects.<n>Our method achieves state-of-the-art accuracy with much faster inference speed, while generalizing well to in-the-wild images.
  arXiv  Detail & Related papers  (2025-03-27T09:45:09Z)
• CoSurfGS:Collaborative 3D Surface Gaussian Splatting with Distributed Learning for Large Scene Reconstruction [68.81212850946318]
  We propose a multi-agent collaborative fast 3DGS surface reconstruction framework based on distributed learning for large-scale surface reconstruction.<n>Specifically, we develop local model compression (LMC) and model aggregation schemes (MAS) to achieve high-quality surface representation of large scenes.<n>Our proposed method can achieve fast and scalable high-fidelity surface reconstruction and photorealistic rendering.
  arXiv  Detail & Related papers  (2024-12-23T14:31:15Z)
• FlexiDreamer: Single Image-to-3D Generation with FlexiCubes [20.871847154995688]
  FlexiDreamer is a novel framework that directly reconstructs high-quality meshes from multi-view generated images. Our approach can generate high-fidelity 3D meshes in the single image-to-3D downstream task with approximately 1 minute.
  arXiv  Detail & Related papers  (2024-04-01T08:20:18Z)
• Zero-Shot Multi-Object Scene Completion [59.325611678171974]
  We present a 3D scene completion method that recovers the complete geometry of multiple unseen objects in complex scenes from a single RGB-D image. Our method outperforms the current state-of-the-art on both synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2024-03-21T17:59:59Z)
• Single-view 3D Mesh Reconstruction for Seen and Unseen Categories [69.29406107513621]
  Single-view 3D Mesh Reconstruction is a fundamental computer vision task that aims at recovering 3D shapes from single-view RGB images. This paper tackles Single-view 3D Mesh Reconstruction, to study the model generalization on unseen categories. We propose an end-to-end two-stage network, GenMesh, to break the category boundaries in reconstruction.
  arXiv  Detail & Related papers  (2022-08-04T14:13:35Z)
• Neural 3D Scene Reconstruction with the Manhattan-world Assumption [58.90559966227361]
  This paper addresses the challenge of reconstructing 3D indoor scenes from multi-view images. Planar constraints can be conveniently integrated into the recent implicit neural representation-based reconstruction methods. The proposed method outperforms previous methods by a large margin on 3D reconstruction quality.
  arXiv  Detail & Related papers  (2022-05-05T17:59:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.