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Efficient Randomized Hierarchy Construction for Interactive Visualization of Large Scale Point Clouds

conferencePaper

DOI:10.1109/DSC.2019.00096
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Authors: Kang Lai / Jiang Jie / Wei Yingmei / Xie Yuxiang

Extracted Abstract:

—Point cloud is widely used in various applications like 3D geographical information system (GIS), cultural heritage preservation, urban planning, etc. Most of these applications require interactive visualization of massive point cloud, which is challenging since their sizes are usually very large. This paper presents a method to construct a hierarchical data structure for point cloud data organization and real-time rendering, with an emphasis on speeding up the construction processing. The overall pipeline consists of the following three steps: first, the spatial extent of the whole dataset is divided into nested blocks; second, data in each block is reorganized using a octree based on random subsampling in a parallel fashion; finally, octree of all blocks are merged into a consistent hierarchy. The effectiveness and efficiency of the a bove approach was demonstrated by applying it to a set of point clouds of varying sizes reconstructed using photogrammetry pipeline. Keywords—point cloud, large scale, hierarchy, visualization, photogrammetry I.

Level 1: Include/Exclude

  • Papers must discuss situated information visualization* (by Willet et al.) in the application domain of CH.
    *A situated data representation is a data representation whose physical presentation is located close to the data’s physical referent(s).
    *A situated visualization is a situated data representation for which the presentation is purely visual – and is typically displayed on a screen.
  • Representation must include abstract data (e.g., metadata).
  • Papers focused solely on digital reconstruction without information visualization aspects are excluded.
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