# WaterSplat-SLAM: Photorealistic Monocular SLAM in Underwater Environment

arXiv: https://arxiv.org/abs/2604.04642 · HTML: https://mlsanity.com/paper/2604.04642/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Apr 2026.

## Built on by (most cited first, 1 of 1)

- [AquaFlow: A Monocular Gaussian Splatting SLAM for Underwater Streaming Reconstruction](https://mlsanity.com/paper/2608.22906/) (2026)

## Abstract (arXiv)

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.