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Topological 3D Modeling Using Indoor Mobile Lidar Data : Volume Xl-4/W5, Issue 1 (11/05/2015)

By Nakagawa, M.

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Book Id: WPLBN0004015137
Format Type: PDF Article :
File Size: Pages 6
Reproduction Date: 2015

Title: Topological 3D Modeling Using Indoor Mobile Lidar Data : Volume Xl-4/W5, Issue 1 (11/05/2015)  
Author: Nakagawa, M.
Volume: Vol. XL-4/W5, Issue 1
Language: English
Subject: Science, Isprs, International
Collections: Periodicals: Journal and Magazine Collection, Copernicus Publications
Publication Date:
Publisher: Copernicus Publications, Göttingen, Germany
Member Page: Copernicus Publications


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Nakagawa, M., Ohhashi, T., Shiozaki, M., Yamamoto, T., & Tanaka, S. (2015). Topological 3D Modeling Using Indoor Mobile Lidar Data : Volume Xl-4/W5, Issue 1 (11/05/2015). Retrieved from

Description: Dept. of Civil Engineering, Shibaura Institute of Technology, Tokyo, Japan. We focus on a region-based point clustering to extract a polygon from a massive point cloud. In the region-based clustering, RANSAC is a suitable approach for estimating surfaces. However, local workspace selection is required to improve a performance in a surface estimation from a massive point cloud. Moreover, the conventional RANSAC is hard to determine whether a point lies inside or outside a surface. In this paper, we propose a method for panoramic rendering-based polygon extraction from indoor mobile LiDAR data. Our aim was to improve region-based point cloud clustering in modeling after point cloud registration. First, we propose a point cloud clustering methodology for polygon extraction on a panoramic range image generated with point-based rendering from a massive point cloud. Next, we describe an experiment that was conducted to verify our methodology with an indoor mobile mapping system in an indoor environment. This experiment was wall-surface extraction using a rendered point cloud from some viewpoints over a wide indoor area. Finally, we confirmed that our proposed methodology could achieve polygon extraction through point cloud clustering from a complex indoor environment.



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