Underwater 3D reconstruction and appearance restoration remain challenging due to the complex optical properties of water, such as wavelength-dependent attenuation and scattering. Existing Neural Radiance Fields (NeRF)-based approaches often suffer from slow rendering and limited practicality, while vanilla 3D Gaussian Splatting (3DGS) lacks an effective mechanism to account for underwater degradation. To address this, we propose WaterClear-GS, a physics-informed reformulation of underwater Gaussian splatting that models degradation as intrinsic Gaussian attributes rather than external medium fields. Furthermore, we adopt a dual-branch optimization strategy that preserves underwater photometric consistency while encouraging a practical separation between restoration-oriented latent clean appearance and degradation. This strategy is enhanced by depth-guided geometry regularization and perception-driven image supervision, together with exposure constraints, spatially adaptive regularization, and physics-informed spectral regularization, which collectively promote spatial coherence and plausible visual appearance. Extensive experiments on standard benchmarks and our collected dataset demonstrate that WaterClear-GS achieves strong performance on both novel view synthesis (NVS) and underwater image restoration (UIR) tasks, while maintaining 160+ FPS real-time rendering. The code will be available at https://sherii888.github.io/WaterClear-GS/.
Figures & tables
Figure 1 : Illustration of underwater imaging and our results. Top : Light from objects is affected by significant attenuation and scattering, which intensify with distance. Bottom : Our method enables high-quality rendering at over 160 FPS and restores plausible clean appearance.
Figure 2 : WaterClear-GS pipeline. Our method augments each Gaussian with wavelength-aware optical proxy parameters for attenuation, backscatter, and veiling light. A shared Gaussian splatting core supports dual-branch rendering: (a) the underwater branch fits the input observations, and (b) the clear branch learns a restoration-oriented latent clean appearance. Depth-guided supervision stabilizes geometry, and exposure, spatial, and spectral regularizations further improve restoration quality.
Figure 3 : Visualization of the OAGM decomposition. The underwater observation is represented using latent clear radiance together with direct-attenuation and backscatter proxies. These components are rendering-oriented variables rather than calibrated medium measurements.
Method
SeaThru-NeRF
Submerged3D
Water3D
Avg.
Avg.
PSNR ↑
SSIM ↑
LPIPS ↓
PSNR ↑
SSIM ↑
LPIPS ↓
PSNR ↑
SSIM ↑
LPIPS ↓
Time ↓
FPS ↑
SeaThru-NeRF [ 22 ]
26.47
0.819
0.282
17.22
0.492
0.598
24.37
0.722
0.460
> 10h
< 0.1
3DGS [ 21 ]
26.71
0.901
0.204
22.71
0.767
0.315
29.19
0.858
0.307
5.37min
234.53
UW-GS [ 49 ]
27.15
0.899
0.202
21.98
0.731
0.356
26.55
0.849
0.315
30.79min
31.72
SeaSplat [ 53 ]
27.84
0.909
0.178
23.14
0.769
0.319
28.86
0.861
0.293
29.38min
60.28
WaterSplatting [ 23 ]
29.57
0.925
0.150
24.55
0.768
0.297
29.14
0.853
0.303
10.11min
75.10
Table 1: Average NVS results on SeaThru-NeRF [ 22 ] , Submerged3D [ 18 ] , and Water3D. Time and FPS are averaged across all scenes. Top-three results are marked by first , second , and third .
Figure 4 : Qualitative results of the NVS task. We present rendered underwater images and their depth maps. The pseudo-depth maps are used as a reference. Zoomed-in regions (highlighted with red bounding boxes) illustrate detailed differences. Our method consistently preserves geometric structures and fine-level details in most cases.
Figure 5 : Qualitative comparison for the UIR task. Across the shown scenes, WaterClear-GS generally produces more balanced colors and clearer local details, while some baselines exhibit underexposure, residual haze, or color shifts.
Method
ΔE00↓
ψˉ↓
NIQE ↓
UIQM ↑
UCIQE ↑
SeaThru-NeRF [ 22 ]
24.73
24.82
5.31
2.91
0.26
UW-GS [ 49 ]
19.13
21.44
4.11
4.16
0.28
SeaSplat [ 53 ]
20.55
21.68
4.13
3.65
0.34
WaterSplatting [ 23 ]
24.45
23.99
4.75
4.08
0.24
RUSplatting [ 18 ]
22.44
22.46
3.99
4.02
0.30
3D-UIR [ 57 ]
22.37
23.40
4.08
3.79
0.35
Table 2: Average UIR quality comparison.
Figure 6 : Results of single-channel and RGB optical modeling.
Figure 7 : Module-level visual ablation. OAGM enables the latent clean branch, reconstruction optimization improves structural fidelity, and restoration optimization further refines the recovered appearance.
Figure 8 : Restoration under controlled synthetic turbidity. The model is optimized from degraded observations without using the clean reference as supervision.
Base
OAGM
Rec. Opt.
Res. Opt.
PSNR ↑
SSIM ↑
LPIPS ↓
✓
✗
✗
✗
28.57
0.902
0.235
✓
✗
✓
✗
29.10
0.924
0.161
✓
✓
✗
✗
28.71
0.906
0.224
✓
✓
✗
✓
28.83
0.905
0.224
✓
✓
✓
✗
29.79
0.929
0.151
✓
✓
✓
✓
29.93
0.931
0.149
Table 3 : Ablation study of the main components on the SeaThru-NeRF dataset. Base denotes our 3DGS backbone trained with the same hyperparameters as WaterClear-GS.
Setting
PSNR ↑
SSIM ↑
LPIPS ↓
ΔE00↓
ψˉ↓
w/o Le
29.80
0.930
0.150
16.26
15.05
w/o Ls
30.00
0.931
0.149
15.84
15.77
w/o Lp
29.97
0.930
0.151
16.49
15.97
Full model
29.93
0.931
0.149
14.56
14.74
Table 4: Restoration-loss ablation on SeaThru-NeRF. ΔE00 and ψˉ measure latent clear-view color fidelity.
Underwater scene reconstruction is essential for immersive exploration of aquatic environments, yet remains challenging due to complex participating-media effects such as absorption and scattering, as well as the limited field of view (FoV) of conventional cameras. Although combining panoramic imaging with 3D Gaussian Splatting (3DGS) offers a promising direction for photorealistic underwater rendering, traditional 3DGS struggles with both spherical projection distortion and underwater medium degradation. In this paper, we propose \textbf{Underwater360}, a physics-informed omnidirectional 3DGS framework for underwater panoramic scene reconstruction. First, we introduce an Omnidirectional Gaussian Splatting module that performs ray casting directly in spherical camera space instead of relying on 2D projection approximations, thereby reducing geometric distortions under 360∘ FoV. Second, we design a physics-based appearance-medium modeling architecture with pose-conditioned appearance embeddings to explicitly decouple intrinsic scene radiance from depth-dependent backscatter and attenuation, enabling physically grounded scene appearance restoration. Finally, we establish a new panoramic underwater benchmark dataset containing both synthetic and real-world scenes. Extensive experiments demonstrate that Underwater360 achieves superior performance in underwater novel view synthesis and scene appearance restoration, delivering improved rendering quality and cross-view consistency in complex underwater environments. The code and datasets are released at https://github.com/SwcK423/Underwater360
Reconstructing realistic underwater scenes from underwater video remains a meaningful yet challenging task in the multimedia domain. The inherent spatiotemporal degradations in underwater imaging, including caustics, flickering, attenuation, and backscattering, frequently result in inaccurate geometry and appearance in existing 3D reconstruction methods. While a few recent works have explored underwater degradation-aware reconstruction, they often address either spatial or temporal degradation alone, falling short in more real-world underwater scenarios where both types of degradation occur. We propose MarineSTD-GS, a novel 3D Gaussian Splatting-based framework that explicitly models both temporal and spatial degradations for realistic underwater scene reconstruction. Specifically, we introduce two paired Gaussian primitives: Intrinsic Gaussians represent the true scene, while Degraded Gaussians render the degraded observations. The color of each Degraded Gaussian is physically derived from its paired Intrinsic Gaussian via a Spatiotemporal Degradation Modeling (SDM) module, enabling self-supervised disentanglement of realistic appearance from degraded images. To ensure stable training and accurate geometry, we further propose a Depth-Guided Geometry Loss and a Multi-Stage Optimization strategy. We also construct a simulated benchmark with diverse spatial and temporal degradations and ground-truth appearances for comprehensive evaluation. Experiments on both simulated and real-world datasets show that MarineSTD-GS robustly handles spatiotemporal degradations and outperforms existing methods in novel view synthesis with realistic, water-free scene appearances.
Underwater 3D scene reconstruction is crucial for multimedia applications in adverse environments, such as underwater robotic perception and navigation. However, the complexity of interactions between light propagation, water medium, and object surfaces poses significant difficulties for existing methods in accurately simulating their interplay. Additionally, expensive training and rendering costs limit their practical application. Therefore, we propose Tensorized Underwater Gaussian Splatting (TUGS), a compact underwater 3D representation based on physical modeling of complex underwater light fields. TUGS includes a physics-based underwater Adaptive Medium Estimation (AME) module, enabling accurate simulation of both light attenuation and backscatter effects in underwater environments, and introduces Tensorized Densification Strategies (TDS) to efficiently refine the tensorized representation during optimization. TUGS is able to render high-quality underwater images with faster rendering speeds and less memory usage. Extensive experiments on real-world underwater datasets have demonstrated that TUGS can efficiently achieve superior reconstruction quality using a limited number of parameters. The code is available at https://liamlian0727.github.io/TUGS