Abstract
Point cloud data are acquired using 3D scanner, such as a terrestrial laser scanner and land-based mobile mapping systems, in a surveying, mapping, structure maintenance, and environment monitoring. The latest 3D scanners perform a rapid and massive data acquisition. However, the massive data require huge processing time in data sharing, visualization and 3D modeling. We propose a methodology to improve a performance in 3D surface modeling using point cloud data projected into a panoramic space. Thus, we have clarified that our approach can improve workability in 3D modeling. Additionally, we confirmed that acquired 3D point cloud data with terrestrial laser scanner data can be classified on 2D panoramic range image using normal vectors estimated from point cloud. This classification is based on 2D image processing. However, we also confirmed that a result from proposed modeling was equivalent to conventional 3D modeling.
| Original language | English |
|---|---|
| Title of host publication | 34th Asian Conference on Remote Sensing 2013, ACRS 2013 |
| Publisher | Asian Association on Remote Sensing |
| Pages | 1248-1253 |
| Number of pages | 6 |
| Volume | 2 |
| ISBN (Print) | 9781629939100 |
| Publication status | Published - 2013 |
| Event | 34th Asian Conference on Remote Sensing 2013, ACRS 2013 - Bali Duration: 2013 Oct 20 → 2013 Oct 24 |
Other
| Other | 34th Asian Conference on Remote Sensing 2013, ACRS 2013 |
|---|---|
| City | Bali |
| Period | 13/10/20 → 13/10/24 |
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Keywords
- 3D modeling
- 3D scanner
- Multi-layered panoramic range image
- Point cloud
ASJC Scopus subject areas
- Computer Networks and Communications
Cite this
Surface modeling based on point cloud rendering using terrestrial LiDAR data. / Kataoka, Konosuke; Nakagawa, Masafumi.
34th Asian Conference on Remote Sensing 2013, ACRS 2013. Vol. 2 Asian Association on Remote Sensing, 2013. p. 1248-1253.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Surface modeling based on point cloud rendering using terrestrial LiDAR data
AU - Kataoka, Konosuke
AU - Nakagawa, Masafumi
PY - 2013
Y1 - 2013
N2 - Point cloud data are acquired using 3D scanner, such as a terrestrial laser scanner and land-based mobile mapping systems, in a surveying, mapping, structure maintenance, and environment monitoring. The latest 3D scanners perform a rapid and massive data acquisition. However, the massive data require huge processing time in data sharing, visualization and 3D modeling. We propose a methodology to improve a performance in 3D surface modeling using point cloud data projected into a panoramic space. Thus, we have clarified that our approach can improve workability in 3D modeling. Additionally, we confirmed that acquired 3D point cloud data with terrestrial laser scanner data can be classified on 2D panoramic range image using normal vectors estimated from point cloud. This classification is based on 2D image processing. However, we also confirmed that a result from proposed modeling was equivalent to conventional 3D modeling.
AB - Point cloud data are acquired using 3D scanner, such as a terrestrial laser scanner and land-based mobile mapping systems, in a surveying, mapping, structure maintenance, and environment monitoring. The latest 3D scanners perform a rapid and massive data acquisition. However, the massive data require huge processing time in data sharing, visualization and 3D modeling. We propose a methodology to improve a performance in 3D surface modeling using point cloud data projected into a panoramic space. Thus, we have clarified that our approach can improve workability in 3D modeling. Additionally, we confirmed that acquired 3D point cloud data with terrestrial laser scanner data can be classified on 2D panoramic range image using normal vectors estimated from point cloud. This classification is based on 2D image processing. However, we also confirmed that a result from proposed modeling was equivalent to conventional 3D modeling.
KW - 3D modeling
KW - 3D scanner
KW - Multi-layered panoramic range image
KW - Point cloud
UR - http://www.scopus.com/inward/record.url?scp=84903454917&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84903454917&partnerID=8YFLogxK
M3 - Conference contribution
SN - 9781629939100
VL - 2
SP - 1248
EP - 1253
BT - 34th Asian Conference on Remote Sensing 2013, ACRS 2013
PB - Asian Association on Remote Sensing
ER -