Feature-assisted interactive geometry reconstruction in 3D point clouds using incremental region growing

Information

• Publication Type: Journal Paper (without talk)
• Workgroup(s)/Project(s): not specified
• Date: April 2023
• DOI: 10.1016/j.cag.2023.02.004
• ISSN: 1873-7684
• Journal: COMPUTERS & GRAPHICS-UK
• Pages: 12
• Volume: 111
• Publisher: PERGAMON-ELSEVIER SCIENCE LTD
• Pages: 213 – 224
• Keywords: Point clouds, interaction, Segmentation, Reconstruction

Abstract

Reconstructing geometric shapes from point clouds is a common task that is often accomplished by experts manually modeling geometries in CAD-capable software. State-of-the-art workflows based on fully automatic geometry extraction are limited by point cloud density and memory constraints, and require pre- and post-processing by the user. In this work, we present a framework for interactive, user-driven, feature-assisted geometry reconstruction from arbitrarily sized point clouds. Based on seeded region-growing point cloud segmentation, the user interactively extracts planar pieces of geometry and utilizes contextual suggestions to point out plane surfaces, normal and tangential directions, and edges and corners. We implement a set of feature-assisted tools for high-precision modeling tasks in architecture and urban surveying scenarios, enabling instant-feedback interactive point cloud manipulation on large-scale data collected from real-world building interiors and facades. We evaluate our results through systematic measurement of the reconstruction accuracy, and interviews with domain experts who deploy our framework in a commercial setting and give both structured and subjective feedback.

Additional Files and Images

Weblinks

• Entry in reposiTUm (TU Wien Publication Database)
• DOI: 10.1016/j.cag.2023.02.004

BibTeX

@article{szabo-2023-fig,
  title =      "Feature-assisted interactive geometry reconstruction in 3D
               point clouds using incremental region growing",
  author =     "Attila Szabo and Georg Haaser and Harald Steinlechner and
               Andreas Walch and Stefan Maierhofer and Thomas Ortner and
               Eduard Gr\"{o}ller",
  year =       "2023",
  abstract =   "Reconstructing geometric shapes from point clouds is a
               common task that is often accomplished by experts manually
               modeling geometries in CAD-capable software.
               State-of-the-art workflows based on fully automatic geometry
               extraction are limited by point cloud density and memory
               constraints, and require pre- and post-processing by the
               user. In this work, we present a framework for interactive,
               user-driven, feature-assisted geometry reconstruction from
               arbitrarily sized point clouds. Based on seeded
               region-growing point cloud segmentation, the user
               interactively extracts planar pieces of geometry and
               utilizes contextual suggestions to point out plane surfaces,
               normal and tangential directions, and edges and corners. We
               implement a set of feature-assisted tools for high-precision
               modeling tasks in architecture and urban surveying
               scenarios, enabling instant-feedback interactive point cloud
               manipulation on large-scale data collected from real-world
               building interiors and facades. We evaluate our results
               through systematic measurement of the reconstruction
               accuracy, and interviews with domain experts who deploy our
               framework in a commercial setting and give both structured
               and subjective feedback.",
  month =      apr,
  doi =        "10.1016/j.cag.2023.02.004",
  issn =       "1873-7684",
  journal =    "COMPUTERS & GRAPHICS-UK",
  pages =      "12",
  volume =     "111",
  publisher =  "PERGAMON-ELSEVIER SCIENCE LTD",
  pages =      "213--224",
  keywords =   "Point clouds, interaction, Segmentation, Reconstruction",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2023/szabo-2023-fig/",
}