Abstract
Recently, point cloud data are acquired by some platforms, such as a terrestrial laser scanner, land-based mobile mapping system (MMS), and airborne LiDAR. These systems can achieve a rapid and massive point cloud data acquisition for road surveying, mapping, structure maintenance, and environment monitoring. However, massive point cloud data require huge processing time in data sharing, visualization and 3D modeling. Therefore, we have proposed a performance improvement of point cloud processing based on point-based rendering approach. Our point-based rendering can select several projection models, such as a spherical, cylindrical, and orthogonal model. Each model has different advantages and disadvantages. Therefore, we proposed a methodology to select a suitable projection model in some point cloud editing works in a road monitoring, structure monitoring, surveying, and indoor mapping. In this paper, we evaluated each projection models through some experiments using terrestrial LiDAR and MMS data.
| Original language | English |
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| Title of host publication | 35th Asian Conference on Remote Sensing 2014, ACRS 2014: Sensing for Reintegration of Societies |
| Publisher | Asian Association on Remote Sensing |
| Publication status | Published - 2014 |
| Event | 35th Asian Conference on Remote Sensing 2014: Sensing for Reintegration of Societies, ACRS 2014 - Nay Pyi Taw, Myanmar Duration: 2014 Oct 27 → 2014 Oct 31 |
Other
| Other | 35th Asian Conference on Remote Sensing 2014: Sensing for Reintegration of Societies, ACRS 2014 |
|---|---|
| Country/Territory | Myanmar |
| City | Nay Pyi Taw |
| Period | 14/10/27 → 14/10/31 |
Keywords
- Point cloud projection model
- Point-based rendering
- Random point cloud
ASJC Scopus subject areas
- Computer Networks and Communications