The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B2-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 293–300, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-293-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 293–300, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-293-2020

  12 Aug 2020

12 Aug 2020

EFFICIENT LOADING AND VISUALIZATION OF MASSIVE FEATURE-RICH POINT CLOUDS WITHOUT HIERARCHICAL ACCELERATION STRUCTURES

J. Otepka1, G. Mandlburger1, M. Schütz2, N. Pfeifer1, and M. Wimmer2 J. Otepka et al.
  • 1TU Wien, Department of Geodesy and Geoinformation, Vienna, Austria
  • 2TU Wien, Institute of Visual Computing & Human-Centered Technology, Vienna, Austria

Keywords: Visualizing Huge Point Clouds, Parallel File Loading, Attribute Inspection, Real-Time Progress Rendering

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.