Lightweight surface reconstruction method for building point clouds based on deep Hough voting

doi:10.3785/j.issn.1008-973X.2026.02.012

Journal of ZheJiang University (Engineering Science)

2026, Vol. 60

Issue (2): 341-350 DOI: 10.3785/j.issn.1008-973X.2026.02.012

Lightweight surface reconstruction method for building point clouds based on deep Hough voting

Jiazhou CHEN1(

),Xiaohang ZHU1,Yanghui XU1,Yin GAO2,3,Yihui LU4,Zhen MAO4,Shenglong LI4,Chaoquan ZHANG2

1. College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
2. Moganshan Geospatial Information Laboratory, Deqing 313200, China
3. National Geomatics Center of China, Beijing 100830, China
4. Shandong Provincial Institute of Land Surveying and Mapping, Jinan 250102, China

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Abstract

To address missing structures, data redundancy and noise in real-world 3D scenes, a lightweight surface reconstruction method for building point clouds was proposed that reconstructs polygonal mesh models. An efficient framework for building-dataset generation was proposed, automatically producing 5 500 labeled building models. To ease the plane extraction for building point clouds, the building planes were predicted using deep Hough voting, and a face-based non-maximal suppression algorithm (F-NMS) was used to efficiently remove the predicted duplicate and erroneous surfaces. A building plane adjacency prediction module was designed to predict the adjacency of the building planes after the F-NMS. Quantitative experimental results demonstrate that, compared to traditional methods such as PolyFit, the proposed approach exhibits significant advantages in both fitting accuracy and scene adaptability. The polygonal mesh models reconstructed by the proposed method retain the main structural features of the input building point clouds, with storage requirements reduced to less than 1% of the original point cloud data.

Key words： 3D point cloud building simplification 3D reconstruction Hough voting mesh model

Received: 17 July 2025 Published: 03 February 2026

CLC:

TP 391.41

Fund: 国家自然科学基金资助项目（62172367）；浙江省尖兵领雁计划研发攻关计划项目（2025C01073）.

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	Jiazhou CHEN
	Xiaohang ZHU
	Yanghui XU
	Yin GAO
	Yihui LU
	Zhen MAO
	Shenglong LI
	Chaoquan ZHANG

Cite this article:

Jiazhou CHEN,Xiaohang ZHU,Yanghui XU,Yin GAO,Yihui LU,Zhen MAO,Shenglong LI,Chaoquan ZHANG. Lightweight surface reconstruction method for building point clouds based on deep Hough voting. Journal of ZheJiang University (Engineering Science), 2026, 60(2): 341-350.

URL:

https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2026.02.012 OR https://www.zjujournals.com/eng/Y2026/V60/I2/341

基于深度霍夫投票的建筑点云轻量级表面重建

针对实景三维场景中建筑物结构缺失、数据冗余、噪声多等问题，提出新的建筑点云轻量级表面重建方法，进行建筑的多边形网格模型重建. 构建高效的建筑数据集生成框架，自动生成包含5 500个带标签的建筑模型数据. 针对建筑点云中平面提取困难的问题，使用深度霍夫投票预测建筑平面，采用基于面的非极大值抑制算法(F-NMS)有效去除预测的重复面以及错误面. 设计建筑平面相邻关系预测模块，对经过非极大值抑制后的建筑平面进行相邻关系的预测. 定量实验结果表明，与如PolyFit的传统方法相比，所提方法在拟合精度与场景适应性方面均具有显著优势. 使用所提方法重建的建筑多边形网格模型保留了输入建筑点云的主要结构特征，存储量不到原始点云的1%.

关键词： 三维点云, 建筑简化, 三维重建, 霍夫投票, 网格模型

Fig.1 Workflow for polygon mesh reconstruction

Fig.2 Structure of planar prediction network based on deep Hough voting

Fig.3 Input and output of face-based non-maximal suppression algorithm

Fig.4 Paired face attention module

Fig.5 Transformation process of building wireframe model based on plane and adjacency relationship

Fig.6 Polygonal mesh model of building

Fig.7 Results and procedural examples of building point cloud reconstruction

Tab.1 Ablation experiment with different weights for voting points loss function (σ=0.01)

Tab.2 Ablation experiment on paired face attention module (σ=0.01)

Tab.3 Ablation experiments on similarity threshold of facet-based non-maximal suppression algorithm (σ=0.02)

Tab.4 Building simplified performance comparison and evaluation form (σ=0.02)

Fig.8 Visual comparison of different methods for 3D building lightweight reconstruction (σ=0.02)

Fig.9 Visual comparison building simplified noise resistance

Tab.5 Quantitative evaluation results of building simplified noise resistance


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