Underwater 3D reconstruction remains challenging under sparse views, where scattering, absorption, and suspended particles degrade feature correspondences and geometric estimation. Although feed-forward geometry foundation models offer an alternative to conventional Structure-from-Motion, their direct application underwater produces noisy and fragmented geometry that limits subsequent 3D Gaussian Splatting (3DGS). We propose a sparse-view underwater reconstruction framework that adapts feed-forward geometry to underwater degradation and exploits its dense geometric priors for view synthesis. First, we adapt VGGT using LoRA and teacher--student distillation, training on synthetically degraded underwater images while preserving clean geometric supervision. This improves robustness to underwater appearance distortions without modifying the pretrained prediction heads. Second, the predicted dense geometry initialises an intermediate 3DGS representation that generates geometry-guided pseudo-views, increasing view overlap and strengthening feature tracks for subsequent RUSplatting optimisation. Experiments on SeaThru-NeRF and Submerged3D demonstrate improved reconstruction quality under sparse-view conditions. On SeaThru-NeRF, our method improves RUSplatting from 24.37 to 27.11 dB PSNR and increases SSIM from 0.7611 to 0.8634, while achieving the best average PSNR and LPIPS on Submerged3D. These results demonstrate the potential of domain-adapted geometric priors for robust sparse-view underwater 3D reconstruction.
Figures & tables
Figure 1. Qualitative comparison between VGGT (top) and our method (bottom) on the Curasao scene. From left to right: rendered depth, reconstructed point cloud, and RGB rendering. Underwater Geometry in a Forward Pass: Sparse-View 3D Reconstruction
Figure 2. Pipeline overview. LoRA-adapted VGGT predicts dense geometry, a 3DGS proxy renders geometry-guided pseudo-views, COLMAP expands tracks, and RUSplatting performs final optimisation.
SeaThru-NeRF
Submerged3D
Initialisation → Renderer
Iter.
PSNR ↑
SSIM ↑
LPIPS ↓
PSNR ↑
SSIM ↑
LPIPS ↓
COLMAP → 3DGS
30k
25.68
0.7735
0.1965
23.48
0.7385
0.3333
VGGT → 3DGS
30k
20.62
0.5380
0.3066
19.38
0.5001
0.4915
FT-VGGT → 3DGS
30k
22.01
0.6240
0.2726
19.58
0.4998
0.4568
COLMAP → RUSplatting
15k
24.37
0.7611
0.3103
22.97
0.7114
0.3514
VGGT → RUSplatting
15k
19.76
0.4824
0.4463
20.41
0.5177
0.4403
Table 1. Performance comparison. Red, orange, and yellow indicate the best, second-, and third-best results, respectively.
Reliable feedforward underwater 3D reconstruction remains challenging due to severe light attenuation and backscattering, which degrade visual quality and disrupt feature consistency across views, leading to inaccurate multi-view geometry. To address this issue, we propose WAT3R, a feed-forward framework for reconstructing 3D scenes directly from underwater images. By leveraging degradation adaptation as a geometry-constrained process, WAT3R integrates a lightweight neural adaptation module to flexibly account for these underwater imaging effects, thereby improving multi-view reconstruction quality. Implemented in a single forward pass, WAT3R directly and efficiently outputs pixel-aligned 3D point maps and camera poses from underwater videos, allowing a high-quality underwater 3D reconstruction. Experiments conducted on the FLSea, SQUID, and USOD10K datasets show that our method consistently outperforms state-of-the-art approaches on 3D reconstruction tasks, including multi-view/monocular depth estimation and camera pose estimation.
Jiayi Xu, Jiahao Lu, Ziqiang Zheng +4
The Hong Kong University of Science and Technology · The Chinese University of Hong Kong · Peking University
Estimating 3D geometry in underwater environments presents unique challenges due to light attenuation, scattering, and the absence of large-scale, high-quality 3D annotations. Pioneering methods rely on massive dense annotations that are impractical in underwater settings. In this paper, we propose Wat3R, a cross-domain semi-supervised learning framework designed to adapt feed-forward 3D reconstruction models from air to underwater scenes. Uniquely, our method eliminates the need for any annotated underwater data following a teacher-student architecture, that learns robust geometry representations merely on abundant unlabeled real underwater video footage. We also design a cross-view consistency loss that leverages geometric cues from other views to compensate for the information degradation in the current view caused by water attenuation and scattering. Furthermore, considering the lack of comprehensive evaluation benchmarks, we construct Water3D, a diverse dataset covering various water bodies and underwater scenarios, designed for geometric task evaluation. Experimental results demonstrate that Wat3R outperforms current state-of-the-art methods in underwater multi-view depth estimation and point cloud reconstruction. The dataset and code are available at https://github.com/LSXI7/Wat3R .
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
Jiangbei Hu, Weichao Song, Shibo Yu +8
School of Software, Dalian University of Technology · College of Computing and Data Science, Nanyang Technological University