arXiv Computer Vision

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.

arXiv Computer Vision
Sep 25

OceanXL: Large-scale Underwater 3D Gaussian Splatting via Block Partitioning and Adaptive Pruning

OceanXL is a new framework that applies 3D Gaussian Splatting to large-scale underwater scenes by partitioning them into spatially coherent blocks and using adaptive pruning to remove redundant primitives. This divide‑and‑conquer approach improves training efficiency and rendering performance while maintaining global geometric consistency. The authors also release a large underwater dataset and demonstrate that OceanXL achieves favorable scalability, compactness, and efficiency compared to existing baselines, with competitive quality on smaller datasets and smaller model sizes than other underwater methods.

By Haoran Wang, Shaoyu Cai, Adrian Azzarelli, Zhuodong Jiang, Guoxi Huang, Eng Tat Khoo, Brett Seymour, Fan Zhang, David Bull, Nantheera Anantrasirichai
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
arXiv Computer Vision
Aug 31

3D-USE: From Image-Level to Scene-Level Underwater Enhancement

The paper introduces 3D-USE, a two‑stage framework for underwater scene‑level enhancement that learns a persistent, visibility‑enhanced 3D representation from degraded multi‑view observations. First, the Medium Radial Basis Anchor Representation (MediumRBF) builds a medium‑aware Gaussian scene by separating object and medium effects. Then, Appearance Transition Consensus (ATC) transfers 2D underwater image enhancement knowledge into scene‑global and Gaussian‑local targets, which are realized by an Underwater Bilateral Appearance Field (U‑BAF) to render enhanced novel views without a 2D UIE model at inference. Experiments on real underwater scenes demonstrate improved visibility, cross‑view consistency, and preserved reconstruction quality.

By Jieyu Yuan, Yuanlin Zhang, Jihong Li, Chunle Guo, Huimin Lu, Chongyi Li
arXiv Computer Vision
Aug 28

RefracGS: Novel View Synthesis Through Refractive Water Surfaces with 3D Gaussian Ray Tracing

RefracGS is a novel framework for generating novel views through refractive water surfaces. It jointly reconstructs the water surface using a neural height field and the underlying scene with a 3D Gaussian field, employing refraction‑aware Gaussian ray tracing based on Snell’s law. The method achieves high‑fidelity view synthesis, outperforms prior refractive approaches, and offers 15× faster training with real‑time rendering at 200 FPS.

By Yiming Shao, Qiyu Dai, Chong Gao, Guanbin Li, Yequan Wang, He Sun, Qiong Zeng, Baoquan Chen, Wenzheng Chen