Page - 133 - in Proceedings of the OAGM&ARW Joint Workshop - Vision, Automation and Robotics

Fig. 8. Reconstructed surface from nadir aerial imagery. The depicted surface shows the Frauenkirche in Munich, located in the west part of the test area. Therefore, west-facing facades cannot be reconstructed. this fusion concept can be easily put into state-of-the-art mapping pipelines, is able to handle large point clouds due to the tiling concept and can be applied for terrestrial, aerial or satellite based mapping application. ACKNOWLEDGMENT This research was partly funded by BMVIT/BMWFW under COMET programme, project nr. 836630, by Land Steiermark through SFG under project nr. 1000033937, and by the Vienna Business Agency. The authors would like to thank Stefan Cavegn and Norbert Haala for providing the terrestrial laser scanner reference data. REFERENCES [1] A. H. Ahmadabadian, S. Robson, J. Boehm, M. Shortis, K. Wenzel, and D. Fritsch, “A comparison of dense matching algorithms for scaled surface reconstruction using stereo camera rigs,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 78, pp. 157 – 167, 2013. [Online]. Available: http://www.sciencedirect.com/science/ article/pii/S0924271613000452 [2] S. Cavegn, N. Haala, S. Nebiker, M. Rothermel, and P. Tutzauer, “Benchmarking High Density Image Matching for Oblique Airborne Imagery,” ISPRS International Archives of the Photogrammetry, Re- moteSensingandSpatial InformationSciences, pp. 45–52, Aug. 2014. [3] S. Fuhrmann and M. Goesele, “Fusion of depth maps with multiple scales,” ACM Trans. Graph., vol. 30, no. 6, pp. 148:1–148:8, Dec. 2011. [Online]. Available: http://doi.acm.org/10.1145/2070781. 2024182 [4] Y. Furukawa and J. Ponce, “Accurate, dense, and robust multiview stereopsis,” IEEETransactionsonPatternAnalysis andMachine Intel- ligence, vol. 32, no. 8, pp. 1362–1376, Aug 2010. [5] S. Galliani, K. Lasinger, and K. Schindler, “Massively parallel mul- tiview stereopsis by surface normal diffusion,” in IEEE International ConferenceonComputerVision (ICCV), June 2015. [6] H. Hirschmu¨ller, “Stereo processing by semiglobal matching and mu- tual information,” IEEETransactionsonPatternAnalysisandMachine Intelligence, vol. 30, no. 2, pp. 328–341, Feb 2008. [7] M. Isenburg, “Laszip,” Photogrammetric Engineering and Remote Sensing, vol. 79, no. 2, pp. 209–217, 2013. [8] M. Kazhdan and H. Hoppe, “Screened poisson surface reconstruction,” ACM Trans. Graph., vol. 32, no. 3, pp. 29:1–29:13, July 2013. [Online]. Available: http://doi.acm.org/10.1145/2487228.2487237 [9] A. Kuhn, H. Huang, M. Drauschke, and H. Mayer, “Fast probabilistic fusion of 3d point clouds via occupancy grids for scene classification,” ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. III-3, pp. 325–332, 2016. [Online]. Avail- able: http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci. net/III-3/325/2016/ [10] A. Kuhn and H. Mayer, “Incremental division of very large point clouds for scalable 3d surface reconstruction,” in 2015 IEEEInterna- tionalConferenceonComputerVisionWorkshop(ICCVW), Dec 2015, pp. 157–165. [11] A. Kuhn, H. Hirschmu¨ller, and H. Mayer, Multi-Resolution Range Data Fusion for Multi-View Stereo Reconstruction. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013, pp. 41–50. [Online]. Available: http://dx.doi.org/10.1007/978-3-642-40602-7 5 [12] A. Kuhn, H. Hirschmu¨ller, D. Scharstein, and H. Mayer, “A TV prior for high-quality scalable multi-view stereo reconstruction,” International Journal of Computer Vision, pp. 1–16, 2016. [Online]. Available: http://dx.doi.org/10.1007/s11263-016-0946-x [13] R. Pajarola, “Large scale terrain visualization using the restricted quadtree triangulation,” inVisualization, Oct 1998, pp. 19–26. [14] R. Perko and C. Zach, “Globally optimal robust DSM fusion,”Euro- peanJournalofRemoteSensing, vol. 49, pp. 489–511, Sept. 2016. [15] M. Rothermel, N. Haala, and D. Fritsch, “A Median-Based Depthmap Fusion Strategy for the Generation of Oriented Points,” ISPRSAnnals ofPhotogrammetry,RemoteSensingandSpatial InformationSciences, pp. 115–122, June 2016. [16] R. B. Rusu and S. Cousins, “3D is here: Point Cloud Library (PCL),” in IEEEInternationalConferenceonRoboticsandAutomation(ICRA), Shanghai, China, May 9-13 2011. [17] S. M. Seitz, B. Curless, J. Diebel, D. Scharstein, and R. Szeliski, “A comparison and evaluation of multi-view stereo reconstruction algorithms,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, June 2006, pp. 519–528. [18] B. Ummenhofer and T. Brox, “Global, dense multiscale reconstruction for a billion points,” in IEEE International Conference on Computer Vision (ICCV), Dec 2015. [Online]. Available: http: //lmb.informatik.uni-freiburg.de//Publications/2015/UB15 [19] C. Zach, T. Pock, and H. Bischof, “A globally optimal algorithm for robust tv-l1 range image integration,” in IEEEInternationalConference onComputerVision (ICCV), Oct 2007, pp. 1–8. 133