Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/11856
Title: Underwater photogrammetry in very shallow waters: main challenges and caustics effect removal
Authors: Agrafiotis, Panagiotis 
Skarlatos, Dimitrios 
Forbes, Timothy 
Poullis, Charalambos 
Skamantzari, Margarita 
Georgopoulos, Andreas 
Keywords: Caustics;CNN;SfM MVS;Underwater 3D reconstruction
Category: Other Engineering and Technologies
Field: Engineering and Technology
Issue Date: 30-May-2018
Publisher: International Society for Photogrammetry and Remote Sensing
Source: ISPRS TC II Mid-term Symposium “Towards Photogrammetry 2020”, 2018, Riva del Garda, Italy, 4–7 June
metadata.dc.doi: https://doi.org/10.5194/isprs-archives-XLII-2-15-2018
Project: Advanced VR, iMmersive serious games and Augmented REality as tools to raise awareness and access to European underwater CULTURal heritage 
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 le arning 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.
Description: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2018, Volume XLII-2, Pages 15-22
URI: http://ktisis.cut.ac.cy/handle/10488/11856
Rights: © Authors 2018.
Type: Conference Papers
Appears in Collections:Δημοσιεύσεις σε συνέδρια/Conference papers

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