Parameter-free connectivity for point clouds

Information

• Publication Type: Conference Paper
• Workgroup(s)/Project(s):
  • Superhumans - Walking Through Walls
  • Modeling the World at Scale
• Date: February 2024
• ISBN: 978-989-758-679-8
• Publisher: SciTePress, Science and Technology Publications
• Open Access: yes
• Location: Rome
• Lecturer: Diana Marin
• Event: 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2024)
• DOI: 10.5220/0012394900003660
• Booktitle: Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1, HUCAPP and IVAPP
• Pages: 11
• Volume: 1
• Conference date: 27. February 2024 – 29. February 2024
• Pages: 92 – 102
• Keywords: Proximity Graphs, Point Clouds, Connectivity

Abstract

Determining connectivity in unstructured point clouds is a long-standing problem that has still not been addressed satisfactorily. In this paper, we analyze an alternative to the often-used k-nearest neighborhood (kNN) graph - the Spheres of Influence Graph (SIG). We show that the edges that are neighboring each vertex are spatially bounded, which allows for fast computation of SIG. Our approach shows a better encoding of the ground truth connectivity compared to the kNN for a wide range of k, and additionally, it is parameter-free. Our result for this fundamental task offers potential for many applications relying on kNN, e.g., parameter-free normal estimation, and consequently, surface reconstruction, motion planning, simulations, and many more.

Additional Files and Images

Weblinks

• Entry in reposiTUm (TU Wien Publication Database)
• DOI: 10.5220/0012394900003660

BibTeX

@inproceedings{marin-2024-pcp,
  title =      "Parameter-free connectivity for point clouds",
  author =     "Diana Marin and Stefan Ohrhallinger and Michael Wimmer",
  year =       "2024",
  abstract =   "Determining connectivity in unstructured point clouds is a
               long-standing problem that has still not been addressed
               satisfactorily. In this paper, we analyze an alternative to
               the often-used k-nearest neighborhood (kNN) graph - the
               Spheres of Influence Graph (SIG). We show that the edges
               that are neighboring each vertex are spatially bounded,
               which allows for fast computation of SIG. Our approach shows
               a better encoding of the ground truth connectivity compared
               to the kNN for a wide range of k, and additionally, it is
               parameter-free. Our result for this fundamental task offers
               potential for many applications relying on kNN, e.g.,
               parameter-free normal estimation, and consequently, surface
               reconstruction, motion planning, simulations, and many more.",
  month =      feb,
  isbn =       "978-989-758-679-8",
  publisher =  "SciTePress, Science and Technology Publications",
  location =   "Rome",
  event =      "19th International Joint Conference on Computer Vision,
               Imaging and Computer Graphics Theory and Applications (2024)",
  doi =        "10.5220/0012394900003660",
  booktitle =  "Proceedings of the 19th International Joint Conference on
               Computer Vision, Imaging and Computer Graphics Theory and
               Applications - Volume 1, HUCAPP and IVAPP",
  pages =      "11",
  volume =     "1",
  pages =      "92--102",
  keywords =   "Proximity Graphs, Point Clouds, Connectivity",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2024/marin-2024-pcp/",
}