arXiv:2608.22906v1 Announce Type: new Abstract: Recent monocular 3D Gaussian Splatting (3DGS) streaming reconstruction methods have achieved impressive performance by balancing reconstruction quality...
Reconstructing photorealistic scenes in unconstrained underwater environments remains challenging due to severe media-induced light scattering and unpredictable dynamic objects. Recent feed-forward vi...
arXiv:2608.22888v1 Announce Type: new Abstract: Reconstructing photorealistic scenes in unconstrained underwater environments remains challenging due to severe media-induced light scattering and unpr...
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.
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.
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.
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.
The paper introduces a technique for animating water within static 3D Gaussian Splatting (3DGS) scenes, allowing realistic, looping water motion without dynamic capture or physical simulation. It estimates 2D optical flow from images or renderings, lifts it into a 3D Eulerian motion field, and deforms Gaussian primitives accordingly, producing temporally seamless animations. The method maintains real‑time rendering performance and improves visual quality and efficiency compared to prior dynamic Gaussian splatting and 3D cinemagraph approaches.
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.
The paper introduces a technique for animating water within static 3D Gaussian Splatting (3DGS) scenes, allowing realistic motion without dynamic capture or physical simulation. It estimates 2D optical flow from images or renderings, lifts it into a 3D Eulerian motion field, and applies this to deform Gaussian primitives, creating temporally seamless looping animations. The method maintains real‑time rendering performance, improves perceptual realism, and supports free‑viewpoint navigation, outperforming prior dynamic Gaussian splatting and 3D cinemagraph approaches on a new waterfall dataset.
The paper introduces LingGS, an online monocular RGB Gaussian splatting SLAM system that decouples feed‑forward geometry prediction from camera tracking. By leveraging a frozen streaming 3D foundation model as a virtual RGB‑D sensor for depth estimation and an explicit geometric solver for camera motion via feature reprojection, LingGS achieves higher tracking accuracy than learned predictions. Experiments on indoor and outdoor datasets demonstrate significant improvements in reconstruction quality, achieving about 7 FPS on a single RTX 4090 and 2–3× the throughput of prior SLAM systems.
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...