Knowledge Commons of Institute of Automation,CAS
Urban Scene LOD Vectorized Modeling From Photogrammetry Meshes | |
Han, Jiali1,2,3; Zhu, Lingjie1,4; Gao, Xiang5; Hu, Zhanyi1,2,3; Zhou, Liyang6; Liu, Hongmin7; Shen, Shuhan1,2,3 | |
发表期刊 | IEEE TRANSACTIONS ON IMAGE PROCESSING |
ISSN | 1057-7149 |
2021 | |
卷号 | 30页码:7458-7471 |
摘要 | Urban scene modeling is a challenging task for the photogrammetry and computer vision community due to its large scale, structural complexity, and topological delicacy. This paper presents an efficient multistep modeling framework for large-scale urban scenes from aerial images. It takes aerial images and a textured 3D mesh model generated by an image-based modeling system as the input and outputs compact polygon models with semantics at different levels of detail (LODs). Based on the key observation that urban buildings usually have piecewise planar rooftops and vertical walls, we propose a segment-based modeling method, which consists of three major stages: scene segmentation, roof contour extraction, and building modeling. By combining the deep neural network predictions with geometric constraints of the 3D mesh, the scene is first segmented into three classes. Then, for each building mesh, the 2D line segments are detected and used to slice the ground into polygon cells, followed by assigning each cell a roof plane via a MRF optimization. Finally, the LOD model is obtained by extruding cells to their corresponding planes. Compared with direct modeling in 3D space, we transform the mesh into a uniform 2D image grid representation and most of the modeling work is performed in 2D space, which has the advantages of low computational complexity and high robustness. In addition, our method doesn't require any global prior, such as the Manhattan or Atlanta world assumption, making it flexible to model scenes with different characteristics and complexity. Experiments on both single buildings and large-scale urban scenes demonstrate that by combining 2D photometric with 3D geometric information, the proposed algorithm is robust and efficient in urban scene LOD vectorized modeling compared with the state-of-the-art approaches. |
关键词 | Urban reconstruction building modeling Markov random field segment based modeling |
DOI | 10.1109/TIP.2021.3106811 |
关键词[WOS] | ENERGY MINIMIZATION ; RECONSTRUCTION ; SEGMENTATION |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Natural Science Foundation of China[61991423] ; National Natural Science Foundation of China[61873265] ; National Natural Science Foundation of China[61632003] |
项目资助者 | National Natural Science Foundation of China |
WOS研究方向 | Computer Science ; Engineering |
WOS类目 | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000692208400004 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
七大方向——子方向分类 | 三维视觉 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/45933 |
专题 | 多模态人工智能系统全国重点实验室_机器人视觉 |
通讯作者 | Liu, Hongmin; Shen, Shuhan |
作者单位 | 1.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China 2.Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China 3.CASIA–SenseTime Research Group, Beijing 100190, China 4.Alibaba A.I. Labs, Hangzhou 311100, China 5.College of Engineering, Ocean University of China, Qingdao 266100, China 6.SenseTime Group, Ltd., Beijing 100080, China 7.School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China |
第一作者单位 | 模式识别国家重点实验室; 中国科学院自动化研究所 |
通讯作者单位 | 模式识别国家重点实验室; 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Han, Jiali,Zhu, Lingjie,Gao, Xiang,et al. Urban Scene LOD Vectorized Modeling From Photogrammetry Meshes[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2021,30:7458-7471. |
APA | Han, Jiali.,Zhu, Lingjie.,Gao, Xiang.,Hu, Zhanyi.,Zhou, Liyang.,...&Shen, Shuhan.(2021).Urban Scene LOD Vectorized Modeling From Photogrammetry Meshes.IEEE TRANSACTIONS ON IMAGE PROCESSING,30,7458-7471. |
MLA | Han, Jiali,et al."Urban Scene LOD Vectorized Modeling From Photogrammetry Meshes".IEEE TRANSACTIONS ON IMAGE PROCESSING 30(2021):7458-7471. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Urban_Scene_LOD_Vect(8168KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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