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Vectorized indoor surface reconstruction from 3D point cloud with multistep 2D optimization | |
Han, Jiali1,2,3; Rong, Mengqi1,2,3; Jiang, Hanqing4; Liu, Hongmin5; Shen, Shuhan1,2,3 | |
发表期刊 | ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING |
ISSN | 0924-2716 |
2021-08-27 | |
卷号 | 177页码:57-74 |
摘要 | Vectorized reconstruction from indoor point cloud has attracted increasing attention in recent years due to its high regularity and low memory consumption. Compared with aerial mapping of outdoor urban environments, indoor point cloud generated by LiDAR scanning or image-based 3D reconstruction usually contain more clutter and missing areas, which greatly increase the difficulty of vectorized reconstruction. In this paper, we propose an effective multistep pipeline to reconstruct vectorized models from indoor point cloud without the Manhattan or Atlanta world assumptions. The core idea behind our method is the combination of a sequence of 2D segment or cell assembly problems that are defined as global optimizations while reducing the reconstruction complexity and enhancing the robustness to different scenes. The proposed method includes a semantic segmentation stage and a reconstruction stage. First, we segment the permanent structures of indoor scenes, including ceilings, floors, walls and cylinders, from the input data, and then, we reconstruct these structures in sequence. The floorplan is first generated by detecting wall planes and selecting optimal subsets of projected wall segments with Integer Linear Programming (ILP), followed by constructing a 2D arrangement and recovering the ceiling and floor structures by Markov Random Filed (MRF) labeling on the arrangement. Finally, the wall structures are modeled by lifting each edge of the arrangement to a proper height by means of another global optimization. Merging the respective results yields the final model. The experimental results show that the proposed method could obtain accurate and compact vectorized models on both precise LiDAR data and defect-laden MVS data compared with other state-of-the-art approaches. |
关键词 | Indoor reconstruction Vectorized model Multistep 2D optimization LoD Semantic segmentation |
DOI | 10.1016/j.isprsjprs.2021.04.019 |
关键词[WOS] | AUTOMATIC RECONSTRUCTION ; BUILDING RECONSTRUCTION ; ENERGY MINIMIZATION ; MODELS |
收录类别 | SCI |
语种 | 英语 |
资助项目 | 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 | Physical Geography ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
WOS类目 | Geography, Physical ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology |
WOS记录号 | WOS:000660980400004 |
出版者 | ELSEVIER |
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/173211/45353 |
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.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China 3.CASIA-SenseTime Research Group, China 4.SenseTime Research, Hangzhou, Zhejiang, China 5.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,Rong, Mengqi,Jiang, Hanqing,et al. Vectorized indoor surface reconstruction from 3D point cloud with multistep 2D optimization[J]. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,2021,177:57-74. |
APA | Han, Jiali,Rong, Mengqi,Jiang, Hanqing,Liu, Hongmin,&Shen, Shuhan.(2021).Vectorized indoor surface reconstruction from 3D point cloud with multistep 2D optimization.ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,177,57-74. |
MLA | Han, Jiali,et al."Vectorized indoor surface reconstruction from 3D point cloud with multistep 2D optimization".ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 177(2021):57-74. |
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