Visualization and Editing of Large Point Cloud Data Based on External Memory
Seongrae Kim, Dae-Yong Kwon, Byung Chul Kim · Korean Journal of Computational Design and Engineering · 2020
In this paper, we propose a method for storing large point cloud data in an external memory, retrieving it level by level, and visualizing it. The point cloud data is stored in an external memory in the form of an octree folder structure, and level-of-detail (LOD) is classified according to the depth of the tree. Then, when loading the stored point cloud data, the octree nodes to be loaded are selected based on the LOD and camera parameters, and the points in the nodes are imported and visualized using the rendering thread and the loading thread. In addition, a method for editing point cloud in an external memory is proposed. When editing point cloud data, instead of editing all points, the editing functions are applied only to the point cloud in the internal memory. Then, the editing history is stored, and when the editing works are completed, the editing functions are applied to the point cloud in the external memory. Finally, the proposed method was verified by implementing a computer program and experimenting with it.