arXiv Computer Vision

Geometry beneath the Waves: Dense Priors for Sparse-View Underwater 3D Gaussian Splatting

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
Sep 4

Camera Splatting for Continuous View Optimization

The paper introduces Camera Splatting, a novel framework for optimizing camera viewpoints in novel view synthesis. Each camera is represented as a 3D Gaussian (camera splat), and virtual point cameras are positioned near the surface to sample the distribution of these splats. By continuously refining the camera splats to match desired target distributions observed from the point cameras, the method achieves better capture of complex view‑dependent effects such as metallic reflections and detailed textures compared to the Farthest View Sampling approach.

By Gahye Lee, Hyomin Kim, Gwangjin Ju, Jooeun Son, Hyejeong Yoon, Seungyong Lee
arXiv Computer Vision
1d ago

D3GS: Depth, DINO, and RGB Diffusion Co-Guided 3D Gaussian Splatting for Sparse-View Reconstruction

arXiv:2609.22941v1 Announce Type: new Abstract: Novel view synthesis from sparse inputs remains challenging for 3D Gaussian Splatting (3DGS) due to ambiguous geometry, cross-view inconsistency, and m...

By Yunqi Gao, Zhanfeng Liao, Hanzhang Tu, Zhaoqi Su, Guoqing Zheng, Songtao Wang, Hongwen Zhang, Zhou Xue, Leyuan Liu, Yebin Liu