WaterSplat-SLAM: Photorealistic Monocular SLAM in Underwater Environment
Kangxu Wang, Shaofeng Zou, Chenxing Jiang, Yixiang Dai, Siang Chen, Shaojie Shen, Guijin Wang
Abstract
Underwater monocular SLAM is a challenging problem with applications from autonomous underwater vehicles to marine archaeology. However, existing underwater SLAM methods struggle to produce maps with high-fidelity rendering. In this paper, we propose WaterSplat-SLAM, a novel monocular underwater SLAM system that achieves robust pose estimation and photorealistic dense mapping. Specifically, we couple semantic medium filtering into two-view 3D reconstruction prior to enable underwater-adapted camera tracking and depth estimation. Furthermore, we present a semantic-guided rendering and adaptive map management strategy with an online medium-aware Gaussian map, modeling underwater environment in a photorealistic and compact manner. Experiments on multiple underwater datasets demonstrate that WaterSplat-SLAM achieves robust camera tracking and high-fidelity rendering in underwater environments. The code will be realeased at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/KX-Wang77/WaterSplat-SLAM</uri>.
BibTeX
@inproceedings{ral2026_watersplatslamph,
title = {WaterSplat-SLAM: Photorealistic Monocular SLAM in Underwater Environment},
author = {Kangxu Wang and Shaofeng Zou and Chenxing Jiang and Yixiang Dai and Siang Chen and Shaojie Shen and Guijin Wang},
booktitle = {RA-L 2026},
year = {2026}
}