TOPOLOGICAL 3D MODELING USING INDOOR MOBILE LIDAR DATA
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 clo...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2015-05-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-4-W5/13/2015/isprsarchives-XL-4-W5-13-2015.pdf |
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| author | M. Nakagawa T. Yamamoto S. Tanaka M. Shiozaki T. Ohhashi |
| author_facet | M. Nakagawa T. Yamamoto S. Tanaka M. Shiozaki T. Ohhashi |
| author_sort | M. Nakagawa |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-23T19:48:17Z |
| format | Article |
| id | doaj.art-714774a28ba343f2951e7b6a2db315b3 |
| institution | Directory Open Access Journal |
| issn | 1682-1750 2194-9034 |
| language | English |
| last_indexed | 2024-12-23T19:48:17Z |
| publishDate | 2015-05-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-714774a28ba343f2951e7b6a2db315b32022-12-21T17:33:28ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342015-05-01XL-4/W5131810.5194/isprsarchives-XL-4-W5-13-2015TOPOLOGICAL 3D MODELING USING INDOOR MOBILE LIDAR DATAM. Nakagawa0T. Yamamoto1S. Tanaka2M. Shiozaki3T. Ohhashi4Dept. of Civil Engineering, Shibaura Institute of Technology, Tokyo, JapanDept. of Civil Engineering, Shibaura Institute of Technology, Tokyo, JapanDept. of Civil Engineering, Shibaura Institute of Technology, Tokyo, JapanNikon Trimble Co., Ltd., Tokyo, JapanNikon Trimble Co., Ltd., Tokyo, JapanWe 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.http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-4-W5/13/2015/isprsarchives-XL-4-W5-13-2015.pdf |
| spellingShingle | M. Nakagawa T. Yamamoto S. Tanaka M. Shiozaki T. Ohhashi TOPOLOGICAL 3D MODELING USING INDOOR MOBILE LIDAR DATA The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | TOPOLOGICAL 3D MODELING USING INDOOR MOBILE LIDAR DATA |
| title_full | TOPOLOGICAL 3D MODELING USING INDOOR MOBILE LIDAR DATA |
| title_fullStr | TOPOLOGICAL 3D MODELING USING INDOOR MOBILE LIDAR DATA |
| title_full_unstemmed | TOPOLOGICAL 3D MODELING USING INDOOR MOBILE LIDAR DATA |
| title_short | TOPOLOGICAL 3D MODELING USING INDOOR MOBILE LIDAR DATA |
| title_sort | topological 3d modeling using indoor mobile lidar data |
| url | http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-4-W5/13/2015/isprsarchives-XL-4-W5-13-2015.pdf |
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