R-CAUSTIC: Rippling CAUSTICs underwater Image dataset

Version 2 available! Please make sure to download the latest version of the dataset! 

Description

Rippling caustics seem to be the main factor degrading the underwater RGB image quality and affecting the image- based 3D reconstruction process in very shallow waters. These effects are adversely affecting image matching algorithms by throwing off most of them, leading to less accurate matches and causing issues in the Simultaneous Localization and Mapping (SLAM) based navigation of the Remotely Operated Vehicles (ROV) and Autonomous Underwater Vehicles (AUV) on shallow waters. Also, they are the main cause for dissimilarities in the generated textures and orthoimages. In order to fill the gap in the literature regading underwater rippling caustics imagery with real ground truth and reference images, the first real-world underwater caustics benchmark dataset which contains 1465 underwater images is presented. Together with the RGB imagery, the corresponding generated ground truth images are delivered for facilitating the training and testing of machine learning and deep learning methods for image classification. R-CAUSTIC dataset also provides the necessary data to evaluate, at least to some extent, the performance of 3D reconstruction approaches. Data were acquired using a GoPro Hero 4 Black action camera with image dimensions of 4000 x 3000 pixels, focal length of 2.77mm and pixel size of 1.55μm and a tripod. Action cameras are widely used for underwater image acquisition. The dataset was captured in near-shore underwater sites at depths varying from 0.5 to 2m. No artificial light sources were used. Due to the wind, the turbulent surface of the water created dynamic rippling caustics on the seabed. In total 1465 RGB images were collected, separated in 7 different datasets; five of them containing stereo images, one of them tri-stereo images and one consists of multi-stereo imagery acquired in 7 different camera poses.

Publication

The paper is availbale in Open Access here: https://ieeexplore.ieee.org/document/10172291

If you use this dataset please cite it as R-CAUSTIC [Reference].
[Reference]: P. Agrafiotis, K. Karantzalos and A. Georgopoulos, "Seafloor-Invariant Caustics Removal From Underwater Imagery," in IEEE Journal of Oceanic Engineering, vol. 48, no. 4, pp. 1300-1321, Oct. 2023, doi: 10.1109/JOE.2023.3277168.

BibTeX:

@ARTICLE{10172291,  author={Agrafiotis, Panagiotis and Karantzalos, Konstantinos and Georgopoulos, Andreas},  journal={IEEE Journal of Oceanic Engineering},  title={Seafloor-Invariant Caustics Removal From Underwater Imagery},  year={2023},  volume={48},  number={4},  pages={1300-1321},  doi={10.1109/JOE.2023.3277168}}