City-Level 3D Surface Reconstruction with Viewpoint Orientation Partitioning and Scene Completion
- URL: http://arxiv.org/abs/2607.03771v1
- Date: Sat, 04 Jul 2026 08:44:12 GMT
- Title: City-Level 3D Surface Reconstruction with Viewpoint Orientation Partitioning and Scene Completion
- Abstract summary: 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.
- Score: 92.92843111081761
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Multi-view 3D surface reconstruction is a longstanding challenge in computer vision. Although recent large-scale reconstruction methods based on 3D Gaussian Splatting (3DGS) achieve impressive novel-view synthesis, producing high-quality surfaces over large scenes remains difficult, due to complex geometry, long optimization, and limited memory. In this paper, we propose a novel yet simple partitioning method to efficiently and faithfully reconstruct large-scale scene surfaces. Our key insight lies in a scene partitioning method based on viewpoint orientation. This partitioning approach ensures that views with similar orientations are jointly involved for more accurate depth estimations, leading to precise surface reconstructions and balanced computation on multiple GPUs in parallel. In addition, we propose a strategy to detect and repair missing regions in the initial point cloud caused by sparse viewpoints or insufficient textures, thereby further improving the geometric quality. Extensive experiments on the GauU-Scene, MatrixCity, and UrbanScene3D datasets demonstrate that our method outperforms the state-of-the-art approaches in surface reconstruction for large-scale scenes. Project page: https://hanl2010.github.io/VOP-GS.
Related papers
- Optimizing 3D Gaussian Splatting via Point Cloud Upsampling [11.209081297828961]
3D Gaussianting (3DGS) is a technique for creating and rendering 3D scenes.<n>This study presents and evaluates several point cloud upsampling approaches.<n>We show improvements in reconstruction quality across diverse scene types.
arXiv Detail & Related papers (2026-05-30T00:42:17Z) - City-Mesh3R: Simulation-Ready City-Scale 3D Mesh Reconstruction from Multi-View Images [10.051585377704464]
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.
arXiv Detail & Related papers (2026-05-28T17:53:26Z) - 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) - 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) - GausSurf: Geometry-Guided 3D Gaussian Splatting for Surface Reconstruction [79.42244344704154]
GausSurf employs geometry guidance from multi-view consistency in texture-rich areas and normal priors in texture-less areas of a scene.<n>Our method surpasses state-of-the-art methods in terms of reconstruction quality and computation time.
arXiv Detail & Related papers (2024-11-29T03:54:54Z) - GigaGS: Scaling up Planar-Based 3D Gaussians for Large Scene Surface Reconstruction [71.08607897266045]
3D Gaussian Splatting (3DGS) has shown promising performance in novel view synthesis.
We make the first attempt to tackle the challenging task of large-scale scene surface reconstruction.
We propose GigaGS, the first work for high-quality surface reconstruction for large-scale scenes using 3DGS.
arXiv Detail & Related papers (2024-09-10T17:51:39Z) - SAGS: Structure-Aware 3D Gaussian Splatting [53.6730827668389]
We propose a structure-aware Gaussian Splatting method (SAGS) that implicitly encodes the geometry of the scene.
SAGS reflects to state-of-the-art rendering performance and reduced storage requirements on benchmark novel-view synthesis datasets.
arXiv Detail & Related papers (2024-04-29T23:26:30Z) - GS2Mesh: Surface Reconstruction from Gaussian Splatting via Novel Stereo Views [9.175560202201819]
3D Gaussian Splatting (3DGS) has emerged as an efficient approach for accurately representing scenes.
We propose a novel approach for bridging the gap between the noisy 3DGS representation and the smooth 3D mesh representation.
We render stereo-aligned pairs of images corresponding to the original training poses, feed the pairs into a stereo model to get a depth profile, and finally fuse all of the profiles together to get a single mesh.
arXiv Detail & Related papers (2024-04-02T10:13:18Z) - Neural 3D Scene Reconstruction from Multiple 2D Images without 3D
Supervision [41.20504333318276]
We propose a novel neural reconstruction method that reconstructs scenes using sparse depth under the plane constraints without 3D supervision.
We introduce a signed distance function field, a color field, and a probability field to represent a scene.
We optimize these fields to reconstruct the scene by using differentiable ray marching with accessible 2D images as supervision.
arXiv Detail & Related papers (2023-06-30T13:30:48Z)
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.