Visualization of Differences between Spatial Measurements and 3D Planning Data
Article No.: 3, Pages 1 - 5
Abstract
In this paper, a system for detection and visualization of geometric differences between 3D planning data and spatial measurement is presented. Usually construction processes are forward processes without feedback loops, e.g. construction of buildings or 3D printing. In our approach an automatic feedback step adds differences to the original planning data after the construction phase to get well-documented products. Even if the planning data as well as the 3D reconstruction of the final product are 3D data, this is a challenging task due to different structures of the 3D data (e.g., precisely defined corners and exact dimension vs. rough geometric approximation) and different levels of abstraction (hierarchy of construction elements like doors, walls or gears vs. unstructured point clouds). As a first step towards this goal, we visualize these differences between the original planning data and the 3D reconstruction based on the obtained point cloud data.
References
[1]
Andreas Dietze, Marcel Klomann, Yvonne Jung, Michael Englert, Sebastian Rieger, Achim Rehberger, Silvan Hau, and Paul Grimm. 2017. SMULGRAS: A Platform for Smart Multicodal Graphics Search. In Proceedings Web3D ’17(Brisbane, Australia). ACM, New York, USA, 17:1–17:9.
[2]
Jozef Doboš, Carmen Fan, Sebastian Friston, and Charence Wong. 2018. Screen Space 3D Diff: A Fast and Reliable Method for Real-Time 3D Differencing on the Web. In Proceedings of the 23rd Intl. Conference on 3D Web Technology (Poznań, Poland) (Web3D ’18). ACM, New York, NY, USA, 9.
[3]
M. Goesele, N. Snavely, B. Curless, H. Hoppe, and S. M. Seitz. 2007. Multi-View Stereo for Community Photo Collections. In 2007 IEEE 11th International Conference on Computer Vision. 1–8.
[4]
E. Grilli, F. Menna, and F. Remondino. 2017. A Review of Point Clouds Segmentation and Classification Algorithms. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W3 (2017), 339–344.
[5]
M. Klomann, M. Englert, A. Rehberger, A. Dietze, T. Geier, S. Rieger, P. Grimm, and Y. Jung. 2017. NetFlinCS: A Hybrid Cloud-based Framework to Allow Context-based Detection and Surveillance. In Proceedings VSMM ’17. IEEE, 1–8.
[6]
R. A. Newcombe, S. Izadi, O. Hilliges, D. Molyneaux, D. Kim, A. J. Davison, P. Kohi, J. Shotton, S. Hodges, and A. Fitzgibbon. 2011. KinectFusion: Real-time dense surface mapping and tracking. In 2011 10th IEEE International Symposium on Mixed and Augmented Reality. 127–136.
[7]
Y. Sun, C. Wang, J. Li, Z. Zhang, D. Zai, P. Huang, and C. Wen. 2016. Automated segmentation of LiDAR point clouds for building rooftop extraction. In 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). 1472–1475.
[8]
M. Teichmann and M. Capps. 1998. Surface reconstruction with anisotropic density-scaled alpha shapes. In Proceedings Visualization ’98 (Cat. No.98CB36276). 67–72.
[9]
Sebastian Tuttas, Alex Braun, Andre Borrmann, and Uwe Stilla. 2014. Comparision of photogrammetric point clouds with BIM building elements for construction progress monitoring. ISPRS - Intl. Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences 1, 341–345.
[10]
Sebastian Tuttas, Alexander Braun, André Borrmann, and Uwe Stilla. 2017. Acquisition and consecutive registration of photogrammetric point clouds for construction progress monitoring using a 4D BIM. PFG - Journal of Photogrammetry, Remote Sensing and Geoinformation Science 85, 1 (2017), 3–15.
[11]
S Tuttas, U Stilla, A Braun, and A Borrmann. 2015. Validation of Bim Components by Photogrammetric Point Clouds for Construction Site Monitoring. ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences 2(2015).
[12]
W. Zhou, J. Lu, and W. Yue. 2019. A New Semantic Segmentation Method of Point Cloud Based on PointNet and VoxelNet. In 2019 Chinese Control And Decision Conference (CCDC). 803–808.
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Published In
November 2020
201 pages
ISBN:9781450381697
DOI:10.1145/3424616
Copyright © 2020 ACM.
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Published: 09 November 2020
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Web3D '20
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November 9 - 13, 2020
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Overall Acceptance Rate 27 of 71 submissions, 38%
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