TY - GEN
T1 - TOPOLOGICAL 3D MODELING USING INDOOR MOBILE LIDAR DATA
AU - Nakagawa, M.
AU - Yamamoto, T.
AU - Tanaka, S.
AU - Shiozaki, M.
AU - Ohhashi, T.
N1 - Publisher Copyright:
© 2015, Copernicus. All rights reserved.
PY - 2015/5/11
Y1 - 2015/5/11
N2 - 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.
AB - 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.
KW - Indoor mobile mapping
KW - Point cloud
KW - Point-based rendering
UR - http://www.scopus.com/inward/record.url?scp=84933036915&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84933036915&partnerID=8YFLogxK
U2 - 10.5194/isprsarchives-XL-4-W5-13-2015
DO - 10.5194/isprsarchives-XL-4-W5-13-2015
M3 - Conference contribution
AN - SCOPUS:84933036915
T3 - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
SP - 13
EP - 18
BT - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
A2 - Fuse, Takashi
A2 - Nakagawa, M.
PB - International Society for Photogrammetry and Remote Sensing
T2 - ISPRS WG IV/7 and WG V/4 Joint Workshop on Indoor-Outdoor Seamless Modelling, Mapping and Navigation
Y2 - 21 May 2015 through 22 May 2015
ER -