Tri-DehazeGS tackles the problem of reconstructing clean 3D scenes from hazy multi‑view images by decoupling the scene and the haze medium. It represents the scene with Gaussian primitives while modeling the haze as an independent view‑shared tri‑plane field, and composes hazy observations through a physical scattering model. The method introduces Medium‑Decoupled Transmittance Gradient Compensation (MD‑TGC) to balance gradients in low‑transmittance regions, leading to improved novel‑view reconstruction on real and synthetic haze benchmarks.
Existing diffusion-based methods have recently made significant progress in image dehazing. However, they typically neglect the physics of haze formation and reconstruct clean images from pure Gaussian noise, thereby limiting their restoration potential.
arXiv:2610.01876v1 Announce Type: new
Abstract: A surface photographed under even light presents nearly the same appearance from every angle; the same surface under uneven light does not. Exposure ch...
By Tongyu Wu, Jacob Edwards, Ziteng Cui, Caigui Jiang, Cheng Wang
FujinSplat is a method for reconstructing clean 3D scenes from images captured through smoke by operating in the RAW image domain, where the camera’s ISP and the medium’s scattering effects remain separable. It first learns a per‑scene base ISP from hazy RAW captures, then uses a low‑dimensional correction space to fit per‑view corrections at training poses, and finally trains a scene‑agnostic controller to predict these corrections from RAW input. The corrected views supervise a single static 3D Gaussian representation, with a bounded per‑view residual to reconcile cross‑view inconsistencies, achieving superior performance on the RealX3D smoke benchmark compared to physics‑based and restoration‑then‑3DGS baselines.
By Gengjia Chang, Ziteng Cui, Shuhong Liu
arXiv:2610.23454v1 Announce Type: new
Abstract: Atmospheric turbulence degrades image quality in long-range imaging. The degradation is generally modeled with two spatio-temporally varying components...
By Yuka Ogino, Rikuto Otsuka, Yuho Shoji, Takuya Ogawa, Takahiro Toizumi, Atsushi Ito
DPSF-Net is a dual‑prior spatial‑frequency network designed for real‑world remote sensing image dehazing. It combines hazy RGB images with dark channel prior maps as joint inputs, and incorporates a spatial‑frequency residual interaction block, a prior‑guided feature attention module, and a selective kernel complementary fusion module to reduce colour shift, structural distortion, and large‑scale haze. Experiments show that DPSF-Net achieves state‑of‑the‑art performance on the RRSHID benchmark while maintaining a favorable balance of restoration quality, parameter count, and computational complexity.
By Mei Lu, Shangliang Shao, Shanliang Yao
GS-PI introduces an optimization‑decoupled framework that transforms Gaussian Splatting (GS) assets into physically based rendering (PBR) compatible Gaussian assets. By treating PBR material generation as a geometry‑conditioned diffusion process on 3D point clouds, it achieves multi‑view consistency and avoids the pixel‑correspondence problems of 2D diffusion. The method employs a multi‑scale cross‑view conditioning mechanism—combining global semantic priors, photometric cues, and spatial view‑direction signals—to prevent specular highlights from baking into intrinsic colors, and then distills the predicted attributes back into a fully relightable PBR‑GS asset without requiring proxy meshes.
By Jieting Xu, Rengan Xie, Zijian Huang, Zehui Jin, Rui Wang, Yuchi Huo
arXiv:2610.19847v1 Announce Type: new
Abstract: We address inverse rendering with 3D Gaussian Splatting (3DGS) by introducing explicit visibility modeling for relighting as well as material and light...
By Sven Kluge, Oliver Staadt
RIDE‑Splat is a new pipeline that removes windshield reflections from driving videos to produce clean 3D reconstructions using 3D Gaussian Splatting. It assigns world‑consistent transmission to the 3D scene and models the camera‑attached reflection as a shared signed RGB image, learning geometry and reflection in two passes before fitting a clean 3DGS model. The authors also release the RIDE‑Splat Dataset, the first benchmark for reflection removal in driving scenes, and demonstrate that RIDE‑Splat outperforms existing methods on nine aggregate metrics, improving PSNR by up to 3.59 dB and reducing LPIPS by up to 0.0876.
By Kevin David Hayes, Alex Hanson, Allen Tu, Pranav Asthana, Matthias Zwicker, Tom Goldstein
arXiv:2604. 13416v2 Announce Type: replace-cross Abstract: Advances in radiance fields have enabled photorealistic novel view synthesis.
By Cheng-You Lu, Yi-Shan Hung, Wei-Ling Chi, Hao-Ping Wang, Charlie Li-Ting Tsai, Yu-Cheng Chang, Yu-Lun Liu, Thomas Do, Chin-Teng Lin
arXiv:2607.05598v2 Announce Type: replace-cross
Abstract: Novel View Synthesis (NVS) methods, such as 3D Gaussian Splatting (3DGS), rely on the assumption of clean, multi-view consistent, posed input...
By Kristof Overdulve, Lode Jorissen, Nick Michiels
3D Gaussian Splatting (3DGS) employs Gaussian primitives for explicit scene representation, facilitating real-time, high-fidelity reconstruction and novel view synthesis of complex scenes. However, the explicit modeling inherent in 3DGS introduces a gradient bias during optimization, rendering its non-convex optimization process highly susceptible to convergence toward local suboptimal solutions.