Ancient Chinese architecture 3D preservation by merging ground and aerial point clouds

被引:41
作者
Gao, Xiang [1 ,2 ]
Shen, Shuhan [1 ,2 ]
Zhou, Yang [1 ,2 ]
Cui, Hainan [1 ]
Zhu, Lingjie [1 ,2 ]
Hu, Zhanyi [1 ,2 ,3 ]
机构
[1] Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
[2] Univ Chinese Acad Sci, Beijing 100049, Peoples R China
[3] CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing 100049, Peoples R China
基金
美国国家科学基金会;
关键词
Image based modeling; Ground-to-aerial image matching; Ground-to-aerial point cloud merging; Digital heritage; RECONSTRUCTION; ACCURATE; IMAGES;
D O I
10.1016/j.isprsjprs.2018.04.023
中图分类号
P9 [自然地理学];
学科分类号
0705 ; 070501 ;
摘要
Ancient Chinese architecture 3D digitalization and documentation is a challenging task for the image based modeling community due to its architectural complexity and structural delicacy. Currently, an effective approach to ancient Chinese architecture 3D reconstruction is to merge the two point clouds, separately obtained from ground and aerial images by the SIM technique. There are two understanding issues should be specially addressed: (1) it is difficult to find the point matches between the images from different sources due to their remarkable variations in viewpoint and scale; (2) due to the inevitable drift phenomenon in any SfM reconstruction process, the resulting two point clouds are no longer strictly related by a single similarity transformation as it should be theoretically. To address these two issues, a new point cloud merging method is proposed in this work. Our method has the following characteristics: (1) the images are matched by leveraging sparse mesh based image synthesis; (2) the putative point matches are filtered by geometrical consistency check and geometrical model verification; and (3) the two point clouds are merged via bundle adjustment by linking the ground-to-aerial tracks. Extensive experiments show that our method outperforms many of the state-of-the-art approaches in terms of ground-to-aerial image matching and point cloud merging.
引用
收藏
页码:72 / 84
页数:13
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