Volume XLII-2/W7
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W7, 255-261, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W7-255-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W7, 255-261, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W7-255-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.

  12 Sep 2017

12 Sep 2017

COLLISION VISUALIZATION OF A LASER-SCANNED POINT CLOUD OF STREETS AND A FESTIVAL FLOAT MODEL USED FOR THE REVIVAL OF A TRADITIONAL PROCESSION ROUTE

W. Li1, K. Shigeta1, K. Hasegawa1, L. Li1, K. Yano2, and S. Tanaka1 W. Li et al.
  • 1College of Information Science and Engineering, Ritsumeikan University, Kusatsu, Shiga 525-8577, Japan
  • 2Department of Geography, Ritsumeikan University, Kita-ku, Kyoto 603-8577, Japan

Keywords: Laser-scanned Point Cloud, Cultural Heritage, Collision Visualization, Collision, Stochastic Collision Search

Abstract. Recently, laser-scanning technology, especially mobile mapping systems (MMSs), has been applied to measure 3D urban scenes. Thus, it has become possible to simulate a traditional cultural event in a virtual space constructed using measured point clouds. In this paper, we take the festival float procession in the Gion Festival that has a long history in Kyoto City, Japan. The city government plans to revive the original procession route that is narrow and not used at present. For the revival, it is important to know whether a festival float collides with houses, billboards, electric wires or other objects along the original route. Therefore, in this paper, we propose a method for visualizing the collisions of point cloud objects. The advantageous features of our method are (1) a see-through visualization with a correct depth feel that is helpful to robustly determine the collision areas, (2) the ability to visualize areas of high collision risk as well as real collision areas, and (3) the ability to highlight target visualized areas by increasing the point densities there.