摘要
古建筑图像三维重建中图像特征可靠匹配是影响重建效果的一个关键问题.为提高古建筑图像特征的匹配性能,提出了一种基于网格多密度聚类的特征匹配方法.该方法首先采用SIFT算子获取图像特征点;其次对图像进行网格划分,依据网格单元特征点密度确定图像锚单元、邻居单元、边界单元;然后依据局部区域密度相似性确定图像簇;最后对相似簇中的特征点依据最近邻距离比准则进行匹配.在中国古代建筑三维重建数据集和141幅山西晋祠古建筑图像上进行了实验,验证了算法的有效性.
The 3 D reconstruction of Chinese ancient architecture from images is a difficult problem in image based modeling, where the image matching is a key factor. To improve the feature matching performance cross images, a new method, FM_GMC, is proposed in this work, which is based on grid partition and multi-density clustering of key-points in grids. Firstly, SIFT is adopted to extract images key-points. Then images are partitioned into grids and key-points densities in grids are computed, from which anchor cells, neighbor cells, and border cells are determined. Meanwhile, the connected regions of such labeled cells are defined as clusters according to the similarity of local regions. Finally, key-points matching within similar clusters is carried out by the nearest neighbor distance ratio(NNDR). The matching results validate our proposed method on 3 D Reconstruction Dataset and 141 typical architectural images about Jinci.
作者
聂瑶瑶
胡立华
张继福
张素兰
Nie Yaoyao;Hu Lihua;Zhang Jifu;Zhang Sulan(School of Computer Science and Technology,Taiyuan University of Science and Technology,Taiyuan 030024)
出处
《计算机辅助设计与图形学学报》
EI
CSCD
北大核心
2020年第3期437-444,共8页
Journal of Computer-Aided Design & Computer Graphics
基金
国家自然科学基金(61873264)
国家自然科学基金青年科学基金(61602335).
关键词
特征匹配
网格多密度聚类
古建筑图像
SIFT算子
三维重建
feature matching
grid and multi-density clustering
ancient architectural images
SIFT
3D reconstruction
