Abstract
Photogrammetry is a useful tool for creating computer models of archaeological sites for monitoring and for general public outreach. Modeling archaeological sites found in the marine environment is particularly challenging due to danger to divers, the cost of underwater photography equipment and lighting challenges. The automatic acquisition of video footage of underwater marine archaeology sites using an AUV can be an advantageous alternative, yet also incurs its own obstacles. In this paper we present our system and enhancements for applying a standard photogrammetry reconstruction pipeline to underwater sites using video footage captured from an AUV. Our primary contribution is a GPU driven algorithm for texture construction to reduce blur in the final model. We demonstrate the results of our system on a well known wreck site in Malta.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Ffmpeg 4.1. fFmpeg Team (2018)
Dive+ world’s diving community app, life Plus Tech (Shenzhen) Co., Ltd. (2019)
Agisoft-LLC: Agisoft photoscan (2010)
Allène, C., Pons, J.P., Keriven, R.: Seamless image-based texture atlases using multi-band blending. In: 19th International Conference on Pattern Recognition (ICPR 2008), France, p. 10, no. 1 (2008)
Baumberg, A.: Blending images for texturing 3D models. In: Proceedings of the British Machine Vision Conference (2002)
Bazeille, S., Quidu, I., Jaulin, L., Malkasse, J.P.: Automatic underwater image pre-processing. In: CMM 2006 (2006)
Callieri, M., Cignoni, P., Corsini, M., Scopigno, R.: Masked photo blending: mapping dense photographic dataset on high-resolution 3D models. Comput. Graph. 32, 464–473 (2008)
Candeloro, M., Mosciaro, F., Srensen, A.J., Ippoliti, G., Ludvigsen, M.: Sensor-based autonomous path-planner for sea-bottom exploration and mosaicking. In: IFAC Conference on Manoeuvring and Control of Marine Craft, pp. 31–36 (2015)
Chen, Z., Zhou, J., Chen, Y., Wang, G.: 3D texture mapping in multi-view reconstruction. In: Bebis, G., et al. (eds.) ISVC 2012. LNCS, vol. 7431, pp. 359–371. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33179-4_35
Dale, L.K., Amato, N.M.: Probabilistic roadmaps-putting it all together. In: Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No. 01CH37164), vol. 2, pp. 1940–1947, May 2001. https://doi.org/10.1109/ROBOT.2001.932892
Debevec, P.E.: Modeling and rendering architecture from photographs. Ph.D. thesis, University of California at Berkeley, Computer Science Division, Berkeley CA (1996)
Fallon, M.F., Kaess, M., Johannsson, H., Leonard, J.J.: Efficient AUV navigation fusing acoustic ranging and side-scan sonar. In: 2011 IEEE International Conference on Robotics and Automation (ICRA). IEEE Computer Society (2011)
von Fock, S.M.T.S., et al.: Pipeline for reconstruction and visualization of underwater archaeology sites using photogrammetry. In: Proceedings of the 2017 ISCA International Conference on Computers and Their Applications, March 2017
Iqbal, K., Odetayo, M.O., James, A.E., Salam, R.A., Talib, A.Z.: Enhancing the low quality images using unsupervised colour correction method. In: SMC, pp. 1703–1709 (2010)
Li, T.Y., Shie, Y.C.: An incremental learning approach to motion planning with roadmap management. In: Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No. 02CH37292), vol. 4, pp. 3411–3416, May 2002
McCarthy, J., Benjamin, J.: Multi-image photogrammetry for underwater archaeological site recording: an accessible, diver-based approach. J. Marit. Archaeol. 9(1), 95–114 (2014)
Paull, L., Saeedi, S., Seto, M., Li, H.: AUV navigation and localization: a review. IEEE J. Oceanic Eng. 39(1), 131–149 (2014)
Petit, F., Capelle-Laize, A.S., Carre, P.: Underwater image enhancement by attenuation inversionwith quaternions (2009)
Poppinga, J., Birk, A., Pathak, K., Vaskevicius, N.: Fast 6-DOF path planning for autonomous underwater vehicles (AUV) based on 3D plane mapping. In: IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), pp. 1–6. IEEE Press (2011)
Rantanen, M.: Improving probabilistic roadmap methods for fast motion planning. Ph.D. thesis, School of Information Sciences, University of Tampere, August 2014
Ruiz, I.T., De Raucourt, S., Petillot, Y., Lane, D.M.: Concurrent mapping and localization using sidescan sonar. IEEE J. Oceanic Eng. 29(2), 442–456 (2004)
Van Damme, T.: Computer vision photogrammetry for underwater archaeological site recording in a low-visibility environment. Remote Sensing & Spatial Information Sciences, International Archives of the Photogrammetry (2015)
Viswanathan, V.K., et al.: AUV motion-planning for photogrammetric reconstruction of marine archaeological sites. In: IEEE International Conference on Robotics and Automation (2017)
Waechter, M., Moehrle, N., Goesele, M.: Let there be color! large-scale texturing of 3D reconstructions. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 836–850. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_54
Wu, J., et al.: Multi-AUV motion planning for archeological site mapping and photogrammetric reconstruction. J. Field Robot. 36, 1250–1269 (2019)
Yamafune, K., Torres, R., Castro, F.: Multi-image photogrammetry to record and reconstruct underwater shipwreck sites. J. Archaeol. Method Theory 24, 703–725 (2016)
Acknowledgements
We would like to acknowledge the entire 2018 ICEX team. This material is based upon work supported by the National Science Foundation under Grant No. 1460153.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Yager, K., Clark, C., Gambin, T., Wood, Z.J. (2019). Underwater Photogrammetry Reconstruction: GPU Texture Generation from Videos Captured via AUV. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2019. Lecture Notes in Computer Science(), vol 11844. Springer, Cham. https://doi.org/10.1007/978-3-030-33720-9_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-33720-9_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-33719-3
Online ISBN: 978-3-030-33720-9
eBook Packages: Computer ScienceComputer Science (R0)