UNDERWATER PHOTOGRAMMETRY IN VERY SHALLOW WATERS: MAIN CHALLENGES AND CAUSTICS EFFECT REMOVAL
- 1Cyprus University of Technology, Civil Engineering and Geomatics Dept., Lab of Photogrammetric Vision, 2-8 Saripolou str., 3036, Limassol, Cyprus
- 2Immersive and Creative Technologies Lab, Department of Computer Science and Software Engineering, Concordia University, Quebec H3G 1M8 , Canada
- 3National Technical University of Athens, School of Rural and Surveying Engineering, Lab. of Photogrammetry Zografou Campus, 9 Heroon Polytechniou str., 15780, Zografou, Athens, Greece
Keywords: Underwater 3D Reconstruction, SfM MVS, Caustics, CNN
Abstract. In this paper, main challenges of underwater photogrammetry in shallow waters are described and analysed. The very short camera to object distance in such cases, as well as buoyancy issues, wave effects and turbidity of the waters are challenges to be resolved. Additionally, the major challenge of all, caustics, is addressed by a new approach for caustics removal (Forbes et al., 2018) which is applied in order to investigate its performance in terms of SfM-MVS and 3D reconstruction results. In the proposed approach the complex problem of removing caustics effects is addressed by classifying and then removing them from the images. We propose and test a novel solution based on two small and easily trainable Convolutional Neural Networks (CNNs). Real ground truth for caustics is not easily available. We show how a small set of synthetic data can be used to train the network and later transfer the learning to real data with robustness to intra-class variation. The proposed solution results in caustic-free images which can be further used for other tasks as may be needed.