A Review of Indoor Automation Modeling Based on Light Detection and Ranging Point Clouds

被引:5
作者
Cui, Yang [1 ,2 ]
Yang, Bogang [1 ,2 ]
Liu, Peng [1 ,2 ]
Kong, Lingyan [1 ,2 ]
机构
[1] Beijing Inst Surveying & Mapping, 15 Yangfangdian Rd, Haidian, Beijing 100038, Peoples R China
[2] Beijing Key Lab Urban Spatial Informat Engn, 15 Yangfangdian Rd, Haidian, Beijing 100038, Peoples R China
基金
北京市自然科学基金;
关键词
3D indoor modeling; laser scanning sensor; standards; point cloud acquisition and characteristics; object classification; room segmentation; model reconstruction; BUILDING MODELS; 3D RECONSTRUCTION; LIDAR DATA; CLASSIFICATION; SEGMENTATION; EXTRACTION; FRAMEWORK; SCENES; EDGE; BIM;
D O I
10.18494/SAM4211
中图分类号
TH7 [仪器、仪表];
学科分类号
0804 ; 080401 ; 081102 ;
摘要
3D modeling of the indoor environment is essential for urban applications such as indoor navigation, emergency simulations, floor planning, and building construction. With the development of laser scanning sensors, 3D laser scanners can quickly obtain high-density, high -precision 3D coordinates and attribute information, which brings significant advantages in collecting 3D information on indoor scenes. Many studies have been published on the fast reconstruction of 3D models based on point cloud data obtained by various types of laser scanning sensors. In this paper, we review state-of-the-art automated 3D indoor modeling technologies. The 3D modeling standards for indoor environments are introduced, and data acquisition based on laser scanning sensors and characteristics of point clouds are discussed. Indoor object classification and indoor room segmentation are also examined in detail. The 3D indoor reconstruction methods (i.e., line-based, plane-based, and volume-based) are systematically introduced and the advantages and disadvantages of these methods are presented. Future research directions in this field are discussed and summarized. This review can help researchers improve current approaches or develop new techniques for 3D indoor reconstruction.
引用
收藏
页码:247 / 268
页数:22
相关论文
共 113 条
[51]   Relation-Constrained 3D Reconstruction of Buildings in Metropolitan Areas from Photogrammetric Point Clouds [J].
Li, Yuan ;
Wu, Bo .
REMOTE SENSING, 2021, 13 (01) :1-30
[52]  
Lin L. X., 2017, RES IMPLEMENTATION S, P24
[53]  
Lin Y.B., 2019, COMPUT VISION PATTER, DOI [10.13140/RG.2.2.16048.00007, DOI 10.13140/RG.2.2.16048.00007]
[54]   Facet Segmentation-Based Line Segment Extraction for Large-Scale Point Clouds [J].
Lin, Yangbin ;
Wang, Cheng ;
Chen, Bili ;
Zai, Dawei ;
Li, Jonathan .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2017, 55 (09) :4839-4854
[55]   FloorNet: A Unified Framework for Floorplan Reconstruction from 3D Scans [J].
Liu, Chen ;
Wu, Jiaye ;
Furukawa, Yasutaka .
COMPUTER VISION - ECCV 2018, PT VI, 2018, 11210 :203-219
[56]   Automatic Buildings Extraction From LiDAR Data in Urban Area by Neural Oscillator Network of Visual Cortex [J].
Liu, Chun ;
Shi, Beiqi ;
Yang, Xuan ;
Li, Nan ;
Wu, Hangbin .
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2013, 6 (04) :2008-2019
[57]  
Liu Jingbin, 2008, Geomatics and Information Science of Wuhan University, V33, P479
[58]  
Lodha S. K., 2007, J ELECTRON IMAGING, DOI [10.1117/12.714713, DOI 10.1117/12.714713]
[59]   Robust detection of lines using the progressive probabilistic Hough transform [J].
Matas, J ;
Galambos, C ;
Kittler, J .
COMPUTER VISION AND IMAGE UNDERSTANDING, 2000, 78 (01) :119-137
[60]  
Maturana D, 2015, IEEE INT C INT ROBOT, P922, DOI 10.1109/IROS.2015.7353481