Advances in 3D Geoinformation Systems pp 425-428
Working Group II — Acquisition — Position Paper: Data collection and 3D reconstruction
3D Geographical Information Systems need 3D representations of objects and, hence, 3D data acquisition and reconstructions methods. Developments in these two areas, however, are not compatible. While numerous operational sensors for 3D data acquisition are readily available on the market (optical, laser scanning, radar, thermal, acoustic, etc.), 3D reconstruction software offers predominantly manual and semi-automatic tools (e.g. Cyclone, Leica Photogrammetry Suite, PhotoModeler or Sketch-up). The ultimate 3D reconstruction algorithm is still a challenge and a subject of intensive research. Many 3D reconstruction approaches have been investigated, and they can be classified into two large groups, optical image-based and point cloud-based, with respect to the sensor used, which can be mount on different platforms.
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- Döllner, J., T. Kolbe, F. Liecke, T. Sgouros, Takis, K. Teichmann, 2006, The Virtual 3D CityModel of Berlin-Managing, Integrating, and Communicating Complex Urban Information, In: Proceedings of the 25th Urban Data Management Symposium UDMS 2006 in Aalborg, Denmark, May 15–17. 2006.Google Scholar
- van Essen, 2007, Maps Get Real: Digital Maps evolving from mathematical line graphs to virtual reality models. In this book ‘2nd International Workshop on 3D Geo-Information: Requirements, Acquisition, Modelling, Analysis, Visualisation, 12–14 December 2007, Delft, the Netherlands’.Google Scholar
- Henricsson O., and E. Baltsavias, 1997. 3D building reconstruction with ARUBA: a qualitative and quantitative evaluation, Automatic Man-made Object Extraction from Aerial and Space Images (A. Grün, O. Kuebler, and P. Agouris, editors), Birkhaeuser Verlag, Basel, pp. 65–76.Google Scholar
- Jern, M. 2005. Web based 3D visual user interface to flood forecasting system, in: Oosterom, Zlatanova & Fendel (eds.) Geo-information for Disaster Management Springer-Verlag, ISBN 3-540-24988-5, pp. 1021–1039Google Scholar
- Pu, S., Vosselman, G., 2006, Automatic extraction of building features from terrestrial laser scanning International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 36, part 5, Dresden, Germany, September 25–27, 5 p.Google Scholar
- Rottensteiner, F., J. Trinder, S. Clode, K. Kubik, 2005, Automated delineation of roof plans from LIDAR data, ISPRS WG III/3, III/4, V/3 Workshop ‘Laser scanning 2005’, Enschede, the Netherlands, September 12–14, 2005, pp. 221–226.Google Scholar
- Schwalbe, E., H.G. Maas, F. Seidel, 2005, 3D building model generation, from airborne laser scanner data using 2D GIS data and orthogonal point could projections, ISPRS WG III/3, III/4, V/3 Workshop ‘Laser scanning 2005’, Enschede, the Netherlands, September 12–14, 2005, pp209–214.Google Scholar
- Tao, V. 2006, 3D data acquisition and object reconstruction for GIS and AEC, in: Zlatanova & Prosperi (Eds.) 3D Geo-DBMS, in’ 3D large scale data integration: challenges and opportunities, CRC Press, Taylor&Francis Group, pp. 39–56Google Scholar
- Tao, C. V. and Y. Hu, 2001. A Comprehensive Study on The Rational Function Model For Photogrammetric Processing, Photogrammetric Engineering and Remote Sensing, Vol. 67, No. 12, pp. 1347–1358, 2001.Google Scholar
- Wang, Z., Schenk, T., 2000. Building extraction and reconstruction from lidar data, IAPRS, 17–22 July, Amsterdam, vol. 33, part B3, pp. 958–964Google Scholar