作者机构：
[Zhou, Jialu; Zhou, Wenlong; Deng, Yunyuan; Yan, Jinbiao] Hengyang Normal Univ, Coll Geog & Tourism, Hengyang 421002, Peoples R China.;[Fu, Xiangxiang; Fu, XX] East China Normal Univ, Sch Geog Sci, Shanghai 200241, Peoples R China.;[Deng, Yunyuan; Liu, Peilin; Yan, Jinbiao] Natl Local Joint Engn Lab Digital Preservat & Inno, Hengyang 421002, Peoples R China.;[Deng, Yunyuan; Liu, Peilin; Yan, Jinbiao] Cooperat Innovat Ctr Digitalizat Cultural Heritage, Hengyang 421002, Peoples R China.;[Liu, Peilin] Inst Rural Revitalisat, Changsha Coll, Changsha 410022, Peoples R China.
通讯机构：
[Deng, YY ] H;[Fu, XX ] E;Hengyang Normal Univ, Coll Geog & Tourism, Hengyang 421002, Peoples R China.;East China Normal Univ, Sch Geog Sci, Shanghai 200241, Peoples R China.;Natl Local Joint Engn Lab Digital Preservat & Inno, Hengyang 421002, Peoples R China.
关键词：
traditional village;roof feature line;slope segmentation;cloth simulation filter;UAV
摘要：
Traditional Chinese buildings serve as a carrier for the inheritance of traditional culture and national characteristics. In the context of rural revitalization, achieving the 3D reconstruction of traditional village buildings is a crucial technical approach to promoting rural planning, improving living environments, and establishing digital villages. However, traditional algorithms primarily target urban buildings, exhibiting limited adaptability and less ideal feature extraction performance for traditional residential buildings. As a result, guaranteeing the accuracy and reliability of 3D models for different types of traditional buildings remains challenging. In this paper, taking Jingping Village in Western Hunan as an example, we propose a method that combines multiple algorithms based on the slope segmentation of the roof to extract feature lines. Firstly, the VDVI and CSF algorithms are used to extract the building and roof point clouds based on the MVS point cloud. Secondly, according to roof features, village buildings are classified, and a 3D roof point cloud is projected into 2D regular grid data. Finally, the roof slope is segmented via slope direction, and internal and external feature lines are obtained after refinement through Canny edge detection and Hough straight line detection. The results indicate that the CSF algorithm can effectively extract the roofs of I-shaped, L-shaped, and U-shaped traditional buildings. The accuracy of roof surface segmentation based on slope exceeds 99.6%, which is significantly better than the RANSAC algorithm and the region segmentation algorithm. This method is capable of efficiently extracting the characteristic lines of roofs in low-rise buildings within traditional villages. It provides a reference method for achieving the high-precision modeling of traditional village architecture at a low cost and with high efficiency.
作者机构：
[颜金彪; 吴波; 何清华] School of Geography and Environment, Jiangxi Normal University, Nanchang;330022, China;National-Local Joint Engineering Laboratory on Digital Preservation and Innovative Technologies for the Culture of Traditional Villages and Towns, Hengyang Normal University, Hengyang;421002, China;[颜金彪] 330022, China<&wdkj&>National-Local Joint Engineering Laboratory on Digital Preservation and Innovative Technologies for the Culture of Traditional Villages and Towns, Hengyang Normal University, Hengyang
通讯机构：
[Wu, B.] S;School of Geography and Environment, China
作者机构：
[颜金彪; 吴波; 彭馨] School of Geography and Environment, Jiangxi Normal University, Nanchang;330022, China;National-Local Joint Engineering Laboratory on Digital Preservation and Innovative Technologies for the Culture of Traditional Villages and Towns, Hengyang Normal University, Hengyang;421002, China;[颜金彪] 330022, China<&wdkj&>National-Local Joint Engineering Laboratory on Digital Preservation and Innovative Technologies for the Culture of Traditional Villages and Towns, Hengyang Normal University, Hengyang
通讯机构：
[Wu, B.] S;School of Geography and Environment, China
