Volume III-2
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-2, 121–128, 2016
https://doi.org/10.5194/isprs-annals-III-2-121-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-2, 121–128, 2016
https://doi.org/10.5194/isprs-annals-III-2-121-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

  02 Jun 2016

02 Jun 2016

DIRECT IMAGE-TO-GEOMETRY REGISTRATION USING MOBILE SENSOR DATA

C. Kehl1,2, S. J. Buckley1, R. L. Gawthorpe2, I. Viola4, and J. A. Howell3 C. Kehl et al.
  • 1Uni Research AS - Centre for Integrated Petroleum Research (CIPR), Allégaten 41, 5007 Bergen, Norway
  • 2Department of Earth Science, University of Bergen, Allégaten 41, 5007 Bergen, Norway
  • 3Department of Geology & Petroleum Geology, University of Aberdeen, AB24 3UE Aberdeen, UK
  • 4Inst. of Computer Graphics and Algorithms, Technical University of Vienna, 1040 Vienna, Austria

Keywords: Image-to-Geometry, Automatic Pose Estimation, Mobile Devices, Registration Interfaces, Virtual Outcrop Geology

Abstract. Adding supplementary texture and 2D image-based annotations to 3D surface models is a useful next step for domain specialists to make use of photorealistic products of laser scanning and photogrammetry. This requires a registration between the new camera imagery and the model geometry to be solved, which can be a time-consuming task without appropriate automation. The increasing availability of photorealistic models, coupled with the proliferation of mobile devices, gives users the possibility to complement their models in real time. Modern mobile devices deliver digital photographs of increasing quality, as well as on-board sensor data, which can be used as input for practical and automatic camera registration procedures. Their familiar user interface also improves manual registration procedures. This paper introduces a fully automatic pose estimation method using the on-board sensor data for initial exterior orientation, and feature matching between an acquired photograph and a synthesised rendering of the orientated 3D scene as input for fine alignment. The paper also introduces a user-friendly manual camera registration- and pose estimation interface for mobile devices, based on existing surface geometry and numerical optimisation methods. The article further assesses the automatic algorithm’s accuracy compared to traditional methods, and the impact of computational- and environmental parameters. Experiments using urban and geological case studies show a significant sensitivity of the automatic procedure to the quality of the initial mobile sensor values. Changing natural lighting conditions remain a challenge for automatic pose estimation techniques, although progress is presented here. Finally, the automatically-registered mobile images are used as the basis for adding user annotations to the input textured model.