The paper introduces Gaussian Light Field Splatting (GLFS), a Vision Transformer that embeds continuous light‑field priors derived from 2D Gaussian splatting into its self‑attention mechanism. GLFS models scene illumination as a sum of anisotropic Gaussian basis functions, enabling adaptive spatial gain estimation for low‑light images with complex, non‑uniform lighting. To preserve color fidelity and structural detail, the authors add a color vector angular loss and a luminance edge loss, and report that GLFS outperforms existing methods in illumination correction, structure preservation, and perceptual quality.
arXiv:2609.12682v1 Announce Type: new Abstract: Reconstructing 3D scenes under real-world low-light conditions remains challenging due to severe sensor noise, low signal-to-noise ratios, and degraded...
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...
arXiv:2609.37115v1 Announce Type: new Abstract: We revisit the role of appearance modeling in 3D Gaussian Splatting (3DGS) and show that limited expressiveness in view-dependent reflectance is a key...
Low-light imaging often introduces color bias caused by the low signal-to-noise ratio and the image formation process. Although recent low-light image enhancement methods have achieved strong brightness recovery, faithful color restoration remains challenging, manifesting as overall color bias together with local under- and over-saturation.
arXiv:2608.29043v1 Announce Type: new Abstract: Light-effect contamination poses a significant challenge to nighttime visibility enhancement. Most methods suppress light effects by estimating and dec...
Naka-GS is a low‑light 3D Gaussian Splatting framework that jointly enhances observations and improves Gaussian initialization. It introduces Naka‑Guided Chroma Correction, which uses a fixed bionic response as a photometric anchor to learn bounded, frequency‑decoupled luminance and chromatic corrections, preserving fine structures while reducing color and boundary artifacts. A lightweight, non‑learned Point‑Prior Compaction Module refines dense scene priors via voxel pooling and distance‑adaptive pruning, cutting the number of points from 1.00 M to 59.52 K and shortening optimization time by 37.7 % while achieving state‑of‑the‑art PSNR, SSIM, and LPIPS on eight RealX3D scenes.
Restoring high-fidelity remote sensing imagery from extreme low-light degradation is indispensable for reliable Earth observation and downstream machine vision. However, under severe noise and illumination corruption, existing methods suffer from attention drift, erroneously aggregating features across distinct physical boundaries and causing severe structural blurring and color distortion.
arXiv:2608.21847v1 Announce Type: new Abstract: Low-light image enhancement (LLIE) must correct ambiguous exposure without overwriting structure already supported by the input. Generative transport c...
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.
VolS-GS is a relightable Gaussian splatting framework that reconstructs objects from one-light-at-a-time captures and renders them under novel lighting and viewpoints. It addresses the difficulty of modeling non‑local effects such as subsurface scattering by using the spatial support of the Gaussian scene as the domain of a differentiable finite‑volume transport solver, allowing light to propagate through the object's interior. A small network predicts scattering and absorption coefficients for each Gaussian, and the solver redistributes incident light, while a shadow term and regularizer prevent learned shadow and specular terms from dominating the appearance. "whyItMatters":"The approach improves relighting quality on held‑out lights and views across three OLAT benchmarks, demonstrating its effectiveness for realistic rendering of subsurface scattering effects."
Recent advances in 3D Gaussian Splatting have demonstrated unprecedented success in novel view synthesis. However, the substantial inference and storage overhead driven by high-order Spherical Harmonics (SH) are primary bottlenecks for mobile platforms.
PriorGS is a 3D Gaussian Splatting framework for single‑exposure high dynamic range (HDR) novel view synthesis that uses learned priors to guide HDR reconstruction and camera response function (CRF) estimation. It introduces a 3D base‑gain HDR representation, separating HDR radiance into an LDR base and a 3D gain field, which improves robustness to inconsistencies in pseudo‑HDR targets and enables 3D HDR appearance editing. Experiments show PriorGS outperforms existing baselines in HDR reconstruction quality.