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arXiv Computer Vision
Aug 27

Gaussian Splatting Underwater: A Controlled Cross-Regime Study

The paper investigates the performance of Gaussian splatting techniques for 3D reconstruction in underwater settings, evaluating five publicly available systems across datasets with varying turbidity, illumination loss, and colour attenuation, as well as an industrial survey. The study finds that reconstruction quality is more influenced by environmental setup—such as water clarity and illumination geometry—than by the specific algorithmic architecture, with clear water yielding high frame registration rates and artificial moving lights favoring medium‑blind splatting. The authors release all scene builds, per‑run configurations, and evaluation code to support reproducibility.

By Olaya \'Alvarez-Tu\~n\'on, Stella Gra{\ss}hof
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arXiv Computer Vision
Sep 25

WaterClear-GS: Optical-Aware Gaussian Splatting for Underwater Reconstruction and Restoration

WaterClear-GS introduces a physics-informed Gaussian splatting method tailored for underwater 3D reconstruction and appearance restoration. It models underwater degradation as intrinsic Gaussian attributes and employs a dual-branch optimization that separates clean appearance from degradation while preserving photometric consistency. The approach incorporates depth-guided geometry regularization, perception-driven supervision, exposure constraints, adaptive regularization, and spectral regularization, achieving strong novel view synthesis and image restoration performance at over 160 FPS.

By Xinrui Zhang, Yufeng Wang, Zesheng Wang, Dacheng Qi, Wenrui Ding, Shuangkang Fang
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arXiv Computer Vision
Oct 16

RUSplatting: Robust 3D Gaussian Splatting for Sparse-View Underwater Scene Reconstruction

RUSplatting introduces a robust 3D Gaussian splatting framework for reconstructing high‑fidelity underwater scenes from sparse views. The method decouples RGB learning guided by underwater attenuation physics, employs a novel adaptive weighting scheme for frame interpolation, and adds a loss function that reduces noise while preserving edges. Experiments on the newly released Submerged3D dataset show PSNR gains up to 1.90 dB over state‑of‑the‑art methods, improving both visual quality and geometric accuracy for deep‑sea rendering.

By Zhuodong Jiang, Haoran Wang, Guoxi Huang, Brett Seymour, Nantheera Anantrasirichai
roboticsbenchmarks
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