Efficient Loading and Visualization of Massive Feature-Richt Point Clouds Without Hierarchical Acceleration Structures

Johannes Otepka, Gottfried Mandlburger, Markus Schütz, Norbert Pfeifer, Michael Wimmer
Efficient Loading and Visualization of Massive Feature-Richt Point Clouds Without Hierarchical Acceleration Structures
ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLIII-B2-2020:293-300, August 2020. [paper]

Information

  • Publication Type: Journal Paper with Conference Talk
  • Workgroup(s)/Project(s):
  • Date: August 2020
  • Call for Papers: Call for Paper
  • Date (from): 31. August 2020
  • Date (to): 2. September 2020
  • DOI: 10.5194/isprs-archives-XLIII-B2-2020-293-2020
  • Event: XXIV ISPRS Congress (2020 edition)
  • ISSN: 1682-1750
  • Journal: ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
  • Lecturer: Johannes Otepka
  • Location: online
  • Open Access: yes
  • Pages (from): 293
  • Pages (to): 300
  • Volume: XLIII-B2-2020

Abstract

Nowadays, point clouds are the standard product when capturing reality independent of scale and measurement technique. Especially, Dense Image Matching (DIM) and Laser Scanning (LS) are state of the art capturing methods for a great variety of applications producing detailed point clouds up to billions of points. In-depth analysis of such huge point clouds typically requires sophisticated spatial indexing structures to support potentially long-lasting automated non-interactive processing tasks like feature extraction, semantic labelling, surface generation, and the like. Nevertheless, a visual inspection of the point data is often necessary to obtain an impression of the scene, roughly check for completeness, quality, and outlier rates of the captured data in advance. Also intermediate processing results, containing additional per-point computed attributes, may require visual analyses to draw conclusions or to parameterize further processing. Over the last decades a variety of commercial, free, and open source viewers have been developed that can visualise huge point clouds and colorize them based on available attributes. However, they have either a poor loading and navigation performance, visualize only a subset of the points, or require the creation of spatial indexing structures in advance. In this paper, we evaluate a progressive method that is capable of rendering any point cloud that fits in GPU memory in real time without the need of time consuming hierarchical acceleration structure generation. In combination with our multi-threaded LAS and LAZ loaders, we achieve load performance of up to 20 million points per second, display points already while loading, support flexible switching between different attributes, and rendering up to one billion points with visually appealing navigation behaviour. Furthermore, loading times of different data sets for different open source and commercial software packages are analysed.

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BibTeX

@article{OTEPKA-2020-PPC,
  title =      "Efficient Loading and Visualization of Massive Feature-Richt
               Point Clouds Without Hierarchical Acceleration Structures",
  author =     "Johannes Otepka and Gottfried Mandlburger and Markus
               Sch\"{u}tz and Norbert Pfeifer and Michael Wimmer",
  year =       "2020",
  abstract =   "Nowadays, point clouds are the standard product when
               capturing reality independent of scale and measurement
               technique. Especially, Dense Image Matching (DIM) and Laser
               Scanning (LS) are state of the art capturing methods for a
               great variety of applications producing detailed point
               clouds up to billions of points. In-depth analysis of such
               huge point clouds typically requires sophisticated spatial
               indexing structures to support potentially long-lasting
               automated non-interactive processing tasks like feature
               extraction, semantic labelling, surface generation, and the
               like. Nevertheless, a visual inspection of the point data is
               often necessary to obtain an impression of the scene,
               roughly check for completeness, quality, and outlier rates
               of the captured data in advance. Also intermediate
               processing results, containing additional per-point computed
               attributes, may require visual analyses to draw conclusions
               or to parameterize further processing. Over the last decades
               a variety of commercial, free, and open source viewers have
               been developed that can visualise huge point clouds and
               colorize them based on available attributes. However, they
               have either a poor loading and navigation performance,
               visualize only a subset of the points, or require the
               creation of spatial indexing structures in advance. In this
               paper, we evaluate a progressive method that is capable of
               rendering any point cloud that fits in GPU memory in real
               time without the need of time consuming hierarchical
               acceleration structure generation. In combination with our
               multi-threaded LAS and LAZ loaders, we achieve load
               performance of up to 20 million points per second, display
               points already while loading, support flexible switching
               between different attributes, and rendering up to one
               billion points with visually appealing navigation behaviour.
               Furthermore, loading times of different data sets for
               different open source and commercial software packages are
               analysed.",
  month =      aug,
  doi =        "10.5194/isprs-archives-XLIII-B2-2020-293-2020",
  issn =       "1682-1750",
  journal =    "ISPRS - International Archives of the Photogrammetry, Remote
               Sensing and Spatial Information Sciences",
  volume =     "XLIII-B2-2020",
  pages =      "293--300",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2020/OTEPKA-2020-PPC/",
}