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M. Bleier; A. Nüchter (2017)
Publisher: Copernicus Publications
Journal: The International Archives of the Photogrammetry
Languages: English
Types: Article
Subjects: TA1-2040, T, TA1501-1820, Applied optics. Photonics, Engineering (General). Civil engineering (General), Technology

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In-situ calibration of structured light scanners in underwater environments is time-consuming and complicated. This paper presents a self-calibrating line laser scanning system, which enables the creation of dense 3D models with a single fixed camera and a freely moving hand-held cross line laser projector. The proposed approach exploits geometric constraints, such as coplanarities, to recover the depth information and is applicable without any prior knowledge of the position and orientation of the laser projector. By employing an off-the-shelf underwater camera and a waterproof housing with high power line lasers an affordable 3D scanning solution can be built. In experiments the performance of the proposed technique is studied and compared with 3D reconstruction using explicit calibration. We demonstrate that the scanning system can be applied to above-the-water as well as underwater scenes.
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    • Robotics, 2016. ULS-500 underwater laser scanner. http://www.2grobotics.com/products/ underwater-laser-scanner-uls-500/. Online, accessed January 20, 2017.
    • 3D at Depth, 2016. SL1 subsea lidar. http://www.3datdepth. com/. Online, accessed January 20, 2017.
    • Bouguet, J.-Y., Weber, M. and Perona, P., 1999. What do planar shadows tell about scene geometry? In: 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 1, IEEE, pp. 1-8.
    • Bra¨uer-Burchardt, C., Heinze, M., Schmidt, I., Ku¨hmstedt, P. and Notni, G., 2015. Compact handheld fringe projection based underwater 3D-scanner. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences 40(5), pp. 33-39.
    • Bruno, F., Bianco, G., Muzzupappa, M., Barone, S. and Razionale, A., 2011. Experimentation of structured light and stereo vision for underwater 3D reconstruction. ISPRS Journal of Photogrammetry and Remote Sensing 66(4), pp. 508-518.
    • Burns, J., Delparte, D., Gates, R. and Takabayashi, M., 2015. Utilizing underwater three-dimensional modeling to enhance ecological and biological studies of coral reefs. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences 40(5), pp. 61-66.
    • Dancu, A., Fourgeaud, M., Franjcic, Z. and Avetisyan, R., 2014. Underwater reconstruction using depth sensors. In: SIGGRAPH Asia 2014 Technical Briefs, ACM, pp. 2:1-2:4.
    • Digumarti, S. T., Chaurasia, G., Taneja, A., Siegwart, R., Thomas, A. and Beardsley, P., 2016. Underwater 3d capture using a low-cost commercial depth camera. In: 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), IEEE, pp. 1-9.
    • Drap, P., Seinturier, J., Scaradozzi, D., Gambogi, P., Long, L. and Gauch, F., 2007. Photogrammetry for virtual exploration of underwater archeological sites. In: Proceedings of the 21st International Symposium of the International Committee for Architectural Photogrammetry, CIPA, pp. 1-6.
    • Ecker, A., Kutulakos, K. N. and Jepson, A. D., 2007. Shape from planar curves: A linear escape from flatland. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, IEEE, pp. 1-8.
    • Menna, F., Nocerino, E., Troisi, S. and Remondino, F., 2013. A photogrammetric approach to survey floating and semisubmerged objects. In: SPIE Optical Metrology 2013, International Society for Optics and Photonics.
    • Eitel, J. U., Vierling, L. A. and Magney, T. S., 2013. A lightweight, low cost autonomously operating terrestrial laser scanner for quantifying and monitoring ecosystem structural dynamics. Agricultural and Forest Meteorology 180, pp. 86- 96.
    • Furukawa, R. and Kawasaki, H., 2006. Self-calibration of multiple laser planes for 3d scene reconstruction. In: Third International Symposium on 3D Data Processing, Visualization, and Transmission, IEEE, pp. 200-207.
    • Furukawa, R. and Kawasaki, H., 2009. Laser range scanner based on self-calibration techniques using coplanarities and metric constraints. Computer Vision and Image Understanding 113(11), pp. 1118-1129.
    • Furukawa, R., Viet, H. Q. H., Kawasaki, H., Sagawa, R. and Yagi, Y., 2008. One-shot range scanner using coplanarity constraints. In: 2008 15th IEEE International Conference on Image Processing (ICIP), IEEE, pp. 1524-1527.
    • Imaki, M., Ochimizu, H., Tsuji, H., Kameyama, S., Saito, T., Ishibashi, S. and Yoshida, H., 2017. Underwater threedimensional imaging laser sensor with 120-deg wide-scanning angle using the combination of a dome lens and coaxial optics. Optical Engineering.
    • Jokinen, O., 1999. Self-calibration of a light striping system by matching multiple 3-d profile maps. In: Proceedings of the 1999 Second International Conference on 3-D Digital Imaging and Modeling, IEEE, pp. 180-190.
    • Massot-Campos, M. and Oliver-Codina, G., 2014. Underwater laser-based structured light system for one-shot 3D reconstruction. In: Proceedings of IEEE Sensors 2014, IEEE, pp. 1138- 1141.
    • Massot-Campos, M. and Oliver-Codina, G., 2015. Optical sensors and methods for underwater 3d reconstruction. Sensors 15(12), pp. 31525-31557.
    • Morinaga, H., Baba, H., Visentini-Scarzanella, M., Kawasaki, H., Furukawa, R. and Sagawa, R., 2015. Underwater active oneshot scan with static wave pattern and bundle adjustment. In: Pacific-Rim Symposium on Image and Video Technology, Springer, pp. 404-418.
    • Olson, E., 2011. Apriltag: A robust and flexible visual fiducial system. In: 2011 IEEE International Conference on Robotics and Automation (ICRA), IEEE, pp. 3400-3407.
    • Salvi, J., Pages, J. and Batlle, J., 2004. Pattern codification strategies in structured light systems. Pattern recognition 37(4), pp. 827-849.
    • Shortis, M., 2015. Calibration techniques for accurate measurements by underwater camera systems. Sensors 15(12), pp. 30810-30826.
    • Steger, C., 1998. An unbiased detector of curvilinear structures. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(2), pp. 113-125.
    • T o¨rnblom, N., 2010. Underwater 3D surface scanning using structured light. Master's thesis, Uppsala University.
    • Van den Heuvel, F. A., 1998. 3D reconstruction from a single image using geometric constraints. ISPRS Journal of Photogrammetry and Remote Sensing 53(6), pp. 354-368.
    • Winkelbach, S., Molkenstruck, S. and Wahl, F. M., 2006. Lowcost laser range scanner and fast surface registration approach. In: Joint Pattern Recognition Symposium, Springer, pp. 718- 728.
    • Zagorchev, L. and Goshtasby, A., 2006. A paintbrush laser range scanner. Computer Vision and Image Understanding 101(2), pp. 65-86.
    • Zhang, Z., 2000. A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(11), pp. 1330-1334.
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