测绘通报 ›› 2026, Vol. 0 ›› Issue (8): 174-179.doi: 10.13474/j.cnki.11-2246.2026.0825

• 测绘地理信息技术应用案例 • 上一篇    

基于CityGaussianV2改进的滨海古村落大场景实景三维重建方法

梁金豹1, 陈济爱2   

  1. 1. 福建警察学院警察战术系, 福建 福州 350007;
    2. 福建金创利信息科技发展股份有限公司, 福建 福州 350000
  • 收稿日期:2026-04-16 发布日期:2026-09-12
  • 通讯作者: 陈济爱。E-mail:151456362@qq.com
  • 作者简介:梁金豹(1988—),男,硕士,讲师,研究方向为警察体育教学与训练、体育文化研究。E-mail:344830688@qq.com
  • 基金资助:
    福建省社会科学基金(FJ2023C011);福州市对外科技合作项目(2025-Y-015);福建省创新资金项目(2025C0004)

Improved large-scale real-scene 3D reconstruction method for coastal ancient villages based on CityGaussianV2

Liang Jinbao1, Chen Jiai2   

  1. 1. Department of Police Tactics, Fujian Police College, Fuzhou 350007, China;
    2. Fujian Jinchuangli Information Technology Development Co., Ltd., Fuzhou 350000, China
  • Received:2026-04-16 Published:2026-09-12

摘要: [目的] 滨海历史古村落存在街巷布局复杂、建筑构件繁多、临水区域光照不均等问题,采用现有方法开展大场景实景三维重建时,易产生模型空洞、边缘失真、噪声伪影等缺陷。本文以福州侯官滨海古村落为研究对象,提出一种基于CityGaussianV2的改进型大场景三维重建方法。[方法] 依托无人机五向倾斜摄影采集多视影像数据,构建建筑边缘先验权重约束高斯分裂过程,提升古建筑轮廓与细部构件重建精度;引入自适应核密度估计(AKDE)实现三维点云降噪,有效剔除场景离群噪声。[结果] 试验结果显示,Octree-GS、Mip-Splatting、LightGaussian、CityGaussianV2整体误差分别为4.2、4.0、3.7、4.6 cm,本文方法整体误差为2.8 cm,相较对比方法降低24.3%~39.1%。[结论] 研究成果可支撑历史古村落全域高精度三维建模,为古建筑数字化建档及智慧文旅建设提供技术支撑。

关键词: 实景三维建模, 无人机倾斜摄影, 建筑边缘先验, 三维点云去噪, 建筑保护

Abstract: [Purposes] Aiming at the problems of complex street layout,intricate architectural structures and variable illumination in waterfront areas of coastal historic villages,which easily cause holes,rough edges and noise artifacts in large-scale 3D reconstruction,this paper takes Houguan village in Fuzhou as the research area and proposes an improved large-scale realistic 3D reconstruction framework based on CityGaussianV2. [Methods] Multi-view images are acquired via five-direction oblique photography using UAV.The prior weight of building edges is adopted to guide the splitting candidate optimization,so as to improve the reconstruction accuracy of building contours and component boundaries.Adaptive kernel density estimation(AKDE) is introduced for point cloud denoising to suppress outliers and noise artifacts. [Findings] Experimental results indicate that the overall RMSE values of Octree-GS,Mip-Splatting,LightGaussian and CityGaussianV2 are 4.2,4.0,3.7 and 4.6 cm respectively.The overall RMSE of the proposed method is 2.8 cm,with an error reduction ranging from 24.3% to 39.1% compared with all comparison methods. [Conclusions] The proposed method can provide a technical solution for large-scale and high-precision 3D modeling of historic villages,and satisfy the demands of digital archiving and smart tourism.

Key words: real-scene 3D modeling, unmanned aerial vehicle oblique photography, building edge prior, 3D point cloud denoising, architectural conservation

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