WaterSplat-SLAM: Photorealistic Monocular SLAM in Underwater Environment

arXiv cs.RO / 4/7/2026

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Key Points

WaterSplat-SLAM is presented as a monocular underwater SLAM system aimed at improving both pose estimation robustness and photorealistic dense mapping compared with prior underwater methods.
The approach couples semantic medium filtering with a two-view 3D reconstruction prior to better support underwater-adapted camera tracking and depth estimation.
It introduces semantic-guided rendering and adaptive map management using an online medium-aware Gaussian map to model underwater scenes in a photorealistic yet compact representation.
Experiments on multiple underwater datasets reportedly show strong camera tracking performance alongside high-fidelity rendering quality.

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.