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.
The underwater environment is challenging for 3D reconstruction, because particles suspended in the water scatter and diffuse light, turbidity varies, absorption depends on wavelength, and illuminatio...
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:2609.18737v1 Announce Type: new Abstract: Underwater 3D reconstruction supports applications ranging from marine ecosystem monitoring and subsea inspection to underwater archaeology, education,...
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.
arXiv:2608.22906v1 Announce Type: new Abstract: Recent monocular 3D Gaussian Splatting (3DGS) streaming reconstruction methods have achieved impressive performance by balancing reconstruction quality...
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.
Recent monocular 3D Gaussian Splatting (3DGS) streaming reconstruction methods have achieved impressive performance by balancing reconstruction quality and efficiency. However, extending these framewo...
arXiv:2608. 10589v1 Announce Type: cross Abstract: This paper presents $\pi$-SUB, a physics-informed framework for generating synthetic underwater benchmark datasets that bridges the synthetic-to-real gap for Underwater Image Enhancement (UIE).
This paper presents $π$-SUB, a physics-informed framework for generating synthetic underwater benchmark datasets that bridges the synthetic-to-real gap for Underwater Image Enhancement (UIE). The proposed framework extends the classical underwater image formation model by incorporating depth-dependent downwelling irradiance, biologically resolved absorption, and environmental scattering across all ten Jerlov water types, together with independently controllable residual phenomena.
The paper presents a pipeline for constructing accurate ground‑truth annotations for visual tracking across the air–water interface, synchronizing camera frames with motion‑capture data and correcting underwater projections. It introduces a Cross‑Medium Recovery Policy (CMRP) that uses confidence‑triggered template selection to improve MixFormerV2’s tracking performance without retraining the backbone. CMRP raises Macro Success AUC by 2.95 points and increases tracking coverage while reducing recovery latency.
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.
arXiv:2610.40900v1 Announce Type: cross Abstract: Reliable visual perception is essential for safe autonomous underwater exploration. Yet vision models pretrained on terrestrial images become less re...