City-Level 3D Surface Reconstruction with Viewpoint Orientation Partitioning and Scene Completion

2026-07-04

AI Summary

High-fidelity 3D surface reconstruction of large-scale urban scenes remains challenging due to geometric complexity, prolonged optimization times, and GPU memory constraints. This work proposes a viewpoint-direction-based scene partitioning strategy that groups views with similar orientations for joint optimization, substantially improving depth estimation accuracy and enabling balanced multi-GPU computation. Additionally, a point cloud hole detection and inpainting mechanism is introduced to enhance geometric completeness. Built upon the 3D Gaussian Splatting (3DGS) framework, the method integrates partitioned optimization, parallel computation, and scene completion techniques. Extensive experiments on GauU-Scene, MatrixCity, and UrbanScene3D datasets demonstrate that the proposed approach significantly outperforms state-of-the-art methods in reconstruction quality.

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.

Problem

• 3D surface reconstruction
• large-scale scenes
• multi-view
• geometric quality
• scene completion

Innovation

• Viewpoint Orientation Partitioning
• 3D Gaussian Splatting
• Scene Completion
• Large-scale Surface Reconstruction
• Multi-GPU Parallelization