Fast occlusion-based point cloud exploration

Mohamed Radwan, Stefan Ohrhallinger, Michael Wimmer
Fast occlusion-based point cloud exploration
The Visual Computer Journal,37:2769-2781,September 2021.

Information

  • Publication Type: Journal Paper with Conference Talk
  • Workgroup(s)/Project(s): not specified
  • Date: September 2021
  • Call for Papers: Call for Paper
  • Date (from): 2021
  • Date (to): 2021
  • DOI: 10.1007/s00371-021-02243-x
  • Event: CGI 2021
  • Journal: The Visual Computer Journal
  • Lecturer: Mohamed Radwan
  • Open Access: yes
  • Pages (from): 2769
  • Pages (to): 2781
  • Volume: 37

Abstract

Large-scale unstructured point cloud scenes can be quickly visualized without prior reconstruction by utilizing levels-of-detail structures to load an appropriate subset from out-of-core storage for rendering the current view. However, as soon as we need structures within the point cloud, e.g., for interactions between objects, the construction of state-of-the-art data structures requires O(NlogN) time for N points, which is not feasible in real time for millions of points that are possibly updated in each frame. Therefore, we propose to use a surface representation structure which trades off the (here negligible) disadvantage of single-frame use for both output-dominated and near-linear construction time in practice, exploiting the inherent 2D property of sampled surfaces in 3D. This structure tightly encompasses the assumed surface of unstructured points in a set of bounding depth intervals for each cell of a discrete 2D grid. The sorted depth samples in the structure permit fast surface queries, and on top of that an occlusion graph for the scene comes almost for free. This graph enables novel real-time user operations such as revealing partially occluded objects, or scrolling through layers of occluding objects, e.g., walls in a building. As an example application we showcase a 3D scene exploration framework that enables fast, more sophisticated interactions with point clouds rendered in real time.

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BibTeX

@article{Radwan_2021_Occ,
  title =      "Fast occlusion-based point cloud exploration",
  author =     "Mohamed Radwan and Stefan Ohrhallinger and Michael Wimmer",
  year =       "2021",
  abstract =   "Large-scale unstructured point cloud scenes can be quickly
               visualized without prior reconstruction by utilizing
               levels-of-detail structures to load an appropriate subset
               from out-of-core storage for rendering the current view.
               However, as soon as we need structures within the point
               cloud, e.g., for interactions between objects, the
               construction of state-of-the-art data structures requires
               O(NlogN) time for N points, which is not feasible in real
               time for millions of points that are possibly updated in
               each frame. Therefore, we propose to use a surface
               representation structure which trades off the (here
               negligible) disadvantage of single-frame use for both
               output-dominated and near-linear construction time in
               practice, exploiting the inherent 2D property of sampled
               surfaces in 3D. This structure tightly encompasses the
               assumed surface of unstructured points in a set of bounding
               depth intervals for each cell of a discrete 2D grid. The
               sorted depth samples in the structure permit fast surface
               queries, and on top of that an occlusion graph for the scene
               comes almost for free. This graph enables novel real-time
               user operations such as revealing partially occluded
               objects, or scrolling through layers of occluding objects,
               e.g., walls in a building. As an example application we
               showcase a 3D scene exploration framework that enables fast,
               more sophisticated interactions with point clouds rendered
               in real time.",
  month =      sep,
  doi =        "10.1007/s00371-021-02243-x",
  journal =    "The Visual Computer Journal",
  volume =     "37",
  pages =      "2769--2781",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2021/Radwan_2021_Occ/",
}