Volume XLII-4/W10 | Copyright
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W10, 231-236, 2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  12 Sep 2018

12 Sep 2018


J. Wolf, S. Discher, L. Masopust, S. Schulz, R. Richter, and J. Döllner J. Wolf et al.
  • Hasso Plattner Institute, Faculty of Digital Engineering, University of Potsdam, Germany

Keywords: Ground Penetrating Radar, 3D Point Cloud, Infrastructure Maintenance, Anomaly Inspection, Visualization Tool

Abstract. Ground-penetrating 2D radar scans are captured in road environments for examination of pavement condition and below-ground variations such as lowerings and developing pot-holes. 3D point clouds captured above ground provide a precise digital representation of the road’s surface and the surrounding environment. If both data sources are captured for the same area, a combined visualization is a valuable tool for infrastructure maintenance tasks. This paper presents visualization techniques developed for the combined visual exploration of the data captured in road environments. Main challenges are the positioning of the ground radar data within the 3D environment and the reduction of occlusion for individual data sets. By projecting the measured ground radar data onto the precise trajectory of the scan, it can be displayed within the context of the 3D point cloud representation of the road environment. We show that customizable overlay, filtering, and cropping techniques enable insightful data exploration. A 3D renderer combines both data sources. To enable an inspection of areas of interest, ground radar data can be elevated above ground level for better visibility. An interactive lens approach enables to visualize data sources that are currently occluded by others. The visualization techniques prove to be a valuable tool for ground layer anomaly inspection and were evaluated in a real-world data set. The combination of 2D ground radar scans with 3D point cloud data improves data interpretation by giving context information (e.g., about manholes in the street) that can be directly accessed during evaluation.

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