Abstract
Indoor scene reconstruction from point cloud data provided by Terrestrial laser scanning (TLS) has become an issue of major interest in recent years. However, the raw scanned indoor scene is always complex with severe noise, outliers and incomplete regions, which produces more difficulties for indoor scene modeling. In this paper, we presented an automatic approach to reconstruct the structure of indoor scene from point clouds acquired by registering several scans. Our method first extracts different candidate walls by separating the indoor scene into different planes based on normal variation. Then the boundary of those candidate walls are obtained by projecting them onto 2D planes. We classify the walls into exterior wall and interior wall by clustering. After distinguishing the 3D points belonging to exterior walls, a simple strategy is generated to refine the 3D model of wall structure. The methodology has been tested on three real datasets, which constitute of different varieties of indoor scenes. The results derived reveal that the indoor scene could be correctly extracted and modeled.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Mura, C., Mattausch, O., Villanueva, A.J., Gobbetti, E., Pajarola, R.: Robust reconstruction of interior building structures with multiple rooms under clutter and occlusions. In: International Conference on Computer-Aided Design and Computer Graphics, pp. 52–59 (2013)
Xie, L., Wang, R.: Automatic indoor building reconstruction from mobile laser scanning data. ISPRS - Int. Arch. Photogramm. Remote. Sens. Spat. Inf. Sci. XLII-2/W7, 417–422 (2017)
Mura, C., Mattausch, O., Pajarola, R.: Piecewise-planar reconstruction of multi-room interiors with arbitrary wall arrangements. In: Pacific Conference on Computer Graphics and Applications, pp. 179–188 (2016)
Armeni, I., et al.: 3D semantic parsing of large-scale indoor spaces. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1534–1543 (2016)
Tang, P., Huber, D., Akinci, B., Lipman, R., Lytle, A.: Automatic reconstruction of as-built building information models from laser-scanned point clouds: a review of related techniques. Autom. Constr. 19(7), 829–843 (2010)
Izadi, S., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: ACM Symposium on User Interface Software and Technology, Santa Barbara, CA, USA, October, pp. 559–568 (2011)
Fox, D.: RGB-(D) scene labeling: features and algorithms. In: Computer Vision and Pattern Recognition, pp. 2759–2766 (2012)
Du, H., et al.: Interactive 3D modeling of indoor environments with a consumer depth camera. In: International Conference on Ubiquitous Computing, pp. 75–84 (2011)
Henry, P., Krainin, M., Herbst, E., Ren, X., Fox, D.: RGB-D mapping: using depth cameras for dense 3D modeling of indoor environments. In: The International Symposium on Experimental Robotics, pp. 647–663 (2013)
Macher, H., Landes, T., Grussenmeyer, P.: From point clouds to building information models: 3D semi-automatic reconstruction of indoors of existing buildings. Appl. Sci. 7(10), 1030 (2017)
Chen, K., Lai, Y.K., Wu, Y.X., Martin, R., Hu, S.M.: Automatic semantic modeling of indoor scenes from low-quality RGB-D data using contextual information. ACM Trans. Graph. 33(6), 208 (2014)
Oesau, S., Lafarge, F., Alliez, P.: Indoor scene reconstruction using feature sensitive primitive extraction and graph-cut. ISPRS J. Photogram. Remote Sens. 90(90), 68–82 (2014)
Nan, L., Xie, K., Sharf, A.: A search-classify approach for cluttered indoor scene understanding. ACM Trans. Graph. 31(6), 1–10 (2012)
Wang, R., Xie, L., Chen, D.: Modeling indoor spaces using decomposition and reconstruction of structural elements. Photogram. Eng. Remote Sens. 83, 827–841 (2017)
Ochmann, S., Vock, R., Wessel, R., Klein, R.: Automatic reconstruction of parametric building models from indoor point clouds. Comput. Graph. 54(C), 94–103 (2016)
Wang, J., Xie, Q., Xu, Y., Zhou, L., Ye, N.: Cluttered indoor scene modeling via functional part-guided graph matching. Comput. Aided Geom. Des. 43(C), 82–94 (2016)
Acknowledgments
This work was supported in part by the National Natural Science Foundation of China (No. 61871320,61872291); in part by China Postdoctoral Science Foundation (2014M552469); in part by Key laboratory project of Shaanxi Provincial Education Department (17JS099); in part by Shaanxi Postdoctoral Science Foundation (434015014); in part by Shaanxi Natural Science Foundation (2017JQ6023).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Ning, X., Ma, J., Lv, Z., Xu, Q., Wang, Y. (2019). Structure Reconstruction of Indoor Scene from Terrestrial Laser Scanner. In: El Rhalibi, A., Pan, Z., Jin, H., Ding, D., Navarro-Newball, A., Wang, Y. (eds) E-Learning and Games. Edutainment 2018. Lecture Notes in Computer Science(), vol 11462. Springer, Cham. https://doi.org/10.1007/978-3-030-23712-7_13
Download citation
DOI: https://doi.org/10.1007/978-3-030-23712-7_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-23711-0
Online ISBN: 978-3-030-23712-7
eBook Packages: Computer ScienceComputer Science (R0)