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."
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...
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:2608.29269v1 Announce Type: new Abstract: Relightable interactive scene reconstruction aims to build an editable 3D model from scans of different object arrangements and render new layouts unde...
PureLight introduces a neural approach to estimate the appearance of complex luminaires that are difficult for traditional path tracing, such as small emitters surrounded by multiple specular layers. The method uses light tracing to build paths from emitters to exit surfaces and learns the probability density function of outgoing radiance with a large normalizing flow network, then distills this into a lightweight MLP for efficient inference. Additionally, a sampling network and a blending network are trained to compute direct illumination and composite the luminaire into arbitrary scenes, enabling low‑sample rendering of challenging luminaires.
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.
arXiv:2606. 31637v1 Announce Type: cross Abstract: Intrinsic decomposition which expresses image colors as the product of diffuse albedo and shading, possibly augmented with view-dependent residuals has a long history in image editing as it enables the modification of object colors and textures without altering lighting.
The paper introduces a method that combines casual indoor photographs taken with and without flash to recover geometry, materials, and lighting. By using flash residuals to constrain albedo and BRDF and static lighting to capture specular highlights, the approach employs a hash‑encoded MLP anchored to a 2DGS depth map for view‑consistent material decomposition. Experiments on synthetic and real scenes show improved diffuse color, albedo, roughness, and relighting performance, achieving a 4.17 dB PSNR gain over the best baseline.
DiffusionShadow introduces a diffusion-based shadow caching framework for neural volume rendering, compressing many pre‑computed shadow INRs into a single diffusion model conditioned on lighting direction. The method encodes shadow coefficient volumes as shadow INRs, trains the diffusion model to predict shadow INR weights at inference, and integrates directly with standard INR renderers without extra runtime sampling. Experiments demonstrate faster rendering than traditional approaches while avoiding the large storage overhead of independent INRs, producing shadows that closely match reference results.
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.
arXiv:2610.00749v1 Announce Type: new Abstract: Standard 3D Gaussian Splatting (3DGS) learns geometry and appearance jointly from RGB supervision, making it difficult to isolate how luminance and chr...
arXiv:2603. 07664v3 Announce Type: replace-cross Abstract: The reflective appearance, especially strong and typically near-field specular reflections, poses a fundamental challenge for accurate surface reconstruction and novel view synthesis.
Tri‑DehazeGS is a Gaussian Splatting framework that decouples clean scene reconstruction from atmospheric haze by representing the scene with Gaussian primitives and the haze medium with an independent view‑shared tri‑plane field. It uses a physical scattering model to compose hazy observations and introduces Medium‑Decoupled Transmittance Gradient Compensation (MD‑TGC) to re‑balance gradients in low‑transmittance regions without altering forward rendering. Experiments on real and synthetic haze benchmarks demonstrate that this approach improves clean novel‑view reconstruction.