Vectorized indoor surface reconstruction from 3D point cloud with multistep 2D optimization

doi:10.1016/j.isprsjprs.2021.04.019

CORC > 自动化研究所 > 中国科学院自动化研究所 > 模式识别国家重点实验室 > 机器人视觉团队

	Vectorized indoor surface reconstruction from 3D point cloud with multistep 2D optimization
	Han, Jiali 1,2,3; Rong, Mengqi 1,2,3; Jiang, Hanqing 4; Liu, Hongmin 5; Shen, Shuhan 1,2,3
刊名	ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
	2021-08-01
卷号	177 页码:57-74
关键词	Indoor reconstruction Vectorized model Multistep 2D optimization LoD Semantic segmentation
ISSN号	0924-2716
DOI	10.1016/j.isprsjprs.2021.04.019
通讯作者	Liu, Hongmin(hmliu_82@163.com) ; Shen, Shuhan(shshen@nlpr.ia.ac.cn)
英文摘要	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.
资助项目	National Natural Science Foundation of China[61873265] ; National Natural Science Foundation of China[61632003]
WOS关键词	AUTOMATIC RECONSTRUCTION ; BUILDING RECONSTRUCTION ; ENERGY MINIMIZATION ; MODELS
WOS研究方向	Physical Geography ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种	英语
出版者	ELSEVIER
WOS记录号	WOS:000660980400004
资助机构	National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/45352]
专题	自动化研究所_模式识别国家重点实验室_机器人视觉团队
通讯作者	Liu, Hongmin; Shen, Shuhan
作者单位	1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 3.CASIA SenseTime Res Grp, Beijing, Peoples R China 4.SenseTime Res, Hangzhou, Zhejiang, Peoples R China 5.Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China
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.