Authors:
Salvatore Mario Carota
1
;
Alessandro Privitera
1
;
Daniele Di Mauro
2
;
Antonino Furnari
1
;
2
;
Giovanni Farinella
1
;
2
and
Francesco Ragusa
1
;
2
Affiliations:
1
Department of Mathematics and Computer Science, University of Catania, Viale Andrea Doria, 6, Catania, Italy
;
2
Next Vision s.r.l., Viale Andrea Doria, 6, Catania, Italy
Keyword(s):
Underwater 3D Reconstruction, Neural Rendering, 3D Gaussian Splatting.
Abstract:
We tackle the problem of 3D reconstruction of underwater scenarios using neural rendering techniques. We propose a benchmark adopting the SeaThru-NeRF dataset, performing a systematic analysis by comparing several established methods based on NERF and 3D Gaussian Splatting through a series of experiments. The results were evaluated both quantitatively, using various 2D and 3D metrics, and qualitatively, through a user survey assessing the fidelity of the reconstructed images. This serves to provide critical insight into how to select the optimal techniques for 3D reconstruction of underwater scenarios. The results indicate that, in the context of this application, among the algorithms tested, NeRF-based methods performed better in both mesh generation and novel view synthesis than the 3D Gaussian Splatting based methods.