3D point cloud indoor plane element extraction and optimization methodology

doi:10.13474/j.cnki.11-2246.2021.0266

Abstract

Abstract: The accurate extraction and relationship restoration of indoor plane elements is an important basis for the automatic semantic reconstruction of indoor models. This paper proposes an indoor plane element extraction and optimization method for complex three-dimensional point clouds, which firstly uses area growth and RANSAC planar hybrid segmentation methods to segment indoor point cloud data; and then uses spatial location information of indoor semantic components as well as bounding box and normal vector information, the planar elements of the segmented plane are extracted accurately; then the extracted walls are optimized to achieve the merging of shared walls and solve the problem of indoor wall redundancy; finally, the spatial location information of doors and walls is used to restore the door-wall association relationship. In the experimental part, two sets of experimental data are used:one set is the laser point cloud data of a teaching floor of Shenzhen University, the other set is the standard data of International Society for Photogrammetry and Remote Sensing (ISPRS), and the validity and reliability of the method of this paper are verified by evaluating the experimental results.

Key words: indoor point clouds, plane hybrid segmentation, plane element extraction, shared wall merging, door-wall correlations

CLC Number:

P237

SONG Jinyang, QIAN Jianguo, TANG Shengjun, WANG Weixi, LI Xiaoming. 3D point cloud indoor plane element extraction and optimization methodology[J]. Bulletin of Surveying and Mapping, 2021, 0(9): 15-20,27.

References

[1] TANG S, ZHANG Y, LI Y,et al. Fast and automatic reconstruction of semantically rich 3D indoor maps from low-quality RGB-D sequences[J]. Sensors, 2019, 19(3):533.
[2] OCHMANN S, VOCK R, KLEIN R. Automatic recon-struction of fully volumetric 3D building models from oriented point clouds[J]. ISPRS Journal of Photogrammetry and Remote Sensing,2019,151:251-262.
[3] WANG C, CHO Y K, KIM C. Automatic BIM comp-onent extraction from point clouds of existing buildings for sustainability applications[J]. Automation in Construction, 2015, 56:1-13.
[4] 王雯,任小玲,陈逍遥.一种改进的区域增长彩色3D点云分割算法[J].国外电子测量技术,2018,37(11):10-14.
[5] 王嘉鑫,赵夫群.点云数据预处理研究[J].现代信息科技,2020,4(2):129-130.
[6] 王冲,李锻能,邓君裕,等.改进法线方向的点云实时分割提取平面方法研究[J].计算机测量与控制,2018,26(5):210-213.
[7] 李宝顺,岑红燕,包亚萍,等.基于平面提取的点云数据分割算法[J].计算机应用与软件,2014,31(7):145-148.
[8] 杨泽鑫,程效军,丁琼,等.面向室内场景点云的对象重建[J].测绘通报,2017(6):45-48.
[9] 闫利,谢洪,胡晓斌,等.一种新的点云平面混合分割方法[J].武汉大学学报(信息科学版),2013,38(5):517-521.
[10] 苏云龙,平雪良,李楠.基于RANSAC三维点云的平面提取算法[J].激光与红外,2019,49(6):780-784.
[11] 周春霖,朱合华,李晓军.随机抽样一致性平面拟合及其应用研究[J].计算机工程与应用,2011,47(7):177-179.
[12] 李孟迪,蒋胜平,王红平.基于随机抽样一致性算法的稳健点云平面拟合方法[J].测绘科学,2015,40(1):102-106.
[13] MURA C, MATTAUSCH O, VILLANUEVA A J, et al. Automatic room detection and reconstruction in cluttered indoor environments with complex room layouts[J]. Computers & Graphics,2014,44:20-32.
[14] 陈正兵.基于深度图像的室内三维平面分割方法研究[J].电子设计工程,2016,24(24):158-160.
[15] KHOSHELHAM K,VILARIÑO L D, PETER M, et al. The ISPRS benchmark on indoor modelling preliminary results[J]. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020, XLII-B5:207-211.