TY - GEN
T1 - Point cloud clustering using a panoramic layered range image
AU - Nakagawa, Masafumi
AU - Kataoka, Kounosuke
AU - Ouma, Shouta
PY - 2013/1/1
Y1 - 2013/1/1
N2 - Our aim is to improve region-based point cloud clustering in modeling after point cloud integration. First, we proposed a point cloud clustering methodology on a panoramic layered range image generated with point-based rendering from a massive point cloud. Next, we conducted two experiments to verify our methodology. The results of these experiments confirmed that our proposed methodology can achieve point cloud clustering to extract arbitrary features from complex environments including flat surfaces, slopes and stone steps.
AB - Our aim is to improve region-based point cloud clustering in modeling after point cloud integration. First, we proposed a point cloud clustering methodology on a panoramic layered range image generated with point-based rendering from a massive point cloud. Next, we conducted two experiments to verify our methodology. The results of these experiments confirmed that our proposed methodology can achieve point cloud clustering to extract arbitrary features from complex environments including flat surfaces, slopes and stone steps.
KW - 3D edge extraction
KW - Point cloud clustering
KW - Point-based rendering
KW - Surface extraction
KW - Terrestrial laser scanning
UR - http://www.scopus.com/inward/record.url?scp=84903442019&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84903442019&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84903442019
SN - 9781629939100
T3 - 34th Asian Conference on Remote Sensing 2013, ACRS 2013
SP - 36
EP - 43
BT - 34th Asian Conference on Remote Sensing 2013, ACRS 2013
PB - Asian Association on Remote Sensing
T2 - 34th Asian Conference on Remote Sensing 2013, ACRS 2013
Y2 - 20 October 2013 through 24 October 2013
ER -