The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-4
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4, 623–630, 2018
https://doi.org/10.5194/isprs-archives-XLII-4-623-2018
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4, 623–630, 2018
https://doi.org/10.5194/isprs-archives-XLII-4-623-2018

  19 Sep 2018

19 Sep 2018

INTERACTION AND LOCOMOTION TECHNIQUES FOR THE EXPLORATION OF MASSIVE 3D POINT CLOUDS IN VR ENVIRONMENTS

F. Thiel, S. Discher, R. Richter, and J. Döllner F. Thiel et al.
  • Hasso Plattner Institute, Faculty of Digital Engineering, University of Potsdam, Germany

Keywords: Virtual reality, 3D point clouds, Real-time rendering, Locomotion, Interaction techniques

Abstract. Emerging virtual reality (VR) technology allows immersively exploring digital 3D content on standard consumer hardware. Using in-situ or remote sensing technology, such content can be automatically derived from real-world sites. External memory algorithms allow for the non-immersive exploration of the resulting 3D point clouds on a diverse set of devices with vastly different rendering capabilities. Applications for VR environments raise additional challenges for those algorithms as they are highly sensitive towards visual artifacts that are typical for point cloud depictions (i.e., overdraw and underdraw), while simultaneously requiring higher frame rates (i.e., around 90 fps instead of 30–60 fps). We present a rendering system for the immersive exploration and inspection of massive 3D point clouds on state-of-the-art VR devices. Based on a multi-pass rendering pipeline, we combine point-based and image-based rendering techniques to simultaneously improve the rendering performance and the visual quality. A set of interaction and locomotion techniques allows users to inspect a 3D point cloud in detail, for example by measuring distances and areas or by scaling and rotating visualized data sets. All rendering, interaction and locomotion techniques can be selected and configured dynamically, allowing to adapt the rendering system to different use cases. Tests on data sets with up to 2.6 billion points show the feasibility and scalability of our approach.