arxiv: 2604.13333 · v1 · submitted 2026-04-14 · 💻 cs.CV · cs.GR

Recognition: unknown

SSD-GS: Scattering and Shadow Decomposition for Relightable 3D Gaussian Splatting

Iris Zheng , Guojun Tang , Alexander Doronin , Paul Teal , Fang-Lue Zhang

Authors on Pith no claims yet

Pith reviewed 2026-05-10 15:01 UTC · model grok-4.3

classification 💻 cs.CV cs.GR

keywords 3D Gaussian Splattingrelightable renderingreflectance decompositionsubsurface scatteringshadow modelingnovel illuminationphysically based renderingphotorealistic relighting

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The pith

Decomposing reflectance into diffuse, specular, shadow, and subsurface scattering enables photorealistic relighting of 3D Gaussian Splatting scenes under novel lights.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces SSD-GS as a way to make 3D Gaussian Splatting support accurate changes in lighting after the scene is captured. It does this by splitting the way light interacts with surfaces into four separate parts instead of using simpler or learned approximations. Dedicated modules handle each part: a dipole model for light traveling under the surface, a visibility-based method for shadows, and an angle-dependent model for shiny highlights. Training proceeds by gradually combining these parts so the system learns what belongs to the material and what comes from the light. This separation matters because it produces more believable images when the lights are moved to positions absent from the original capture.

Core claim

SSD-GS is a physically-based relighting framework built on 3D Gaussian Splatting that decomposes reflectance into diffuse, specular, shadow, and subsurface scattering components. It introduces a learnable dipole-based scattering module for subsurface transport, an occlusion-aware shadow formulation that combines visibility estimates with a refinement network, and an enhanced specular component using an anisotropic Fresnel model. Progressive integration of the components during training disentangles lighting from material properties even under unseen illumination, as shown by improved results on the OLAT dataset.

What carries the argument

The four-component reflectance decomposition (diffuse, specular, shadow, subsurface scattering) realized through a dipole scattering module, occlusion-aware shadow integration, and anisotropic Fresnel specular term inside the 3D Gaussian Splatting representation.

If this is right

Superior quantitative and perceptual relighting quality compared with prior 3DGS relighting methods on the OLAT dataset.
Enables downstream controllable light source editing.
Supports interactive scene relighting after reconstruction.
Improves fidelity for anisotropic metals and translucent materials that previous coarse decompositions handled poorly.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same decomposition might support independent editing of material parameters once lighting is factored out.
Combining the four-component model with dynamic scene representations could extend the relighting capability to moving objects.
The explicit separation offers a route to inverse problems such as recovering subsurface properties from images alone.
The method could be tested on outdoor scenes where natural illumination varies continuously rather than the controlled OLAT setup.

Load-bearing premise

That gradually introducing the four components during training will separate lighting effects from intrinsic material properties for lighting setups the model has never encountered.

What would settle it

Train the model on a scene captured under one set of lights, then render the same scene under a single new light direction never seen in training and check whether the output matches a ground-truth photograph taken under that exact new light.

Figures

Figures reproduced from arXiv: 2604.13333 by Alexander Doronin, Fang-Lue Zhang, Guojun Tang, Iris Zheng, Paul Teal.

**Figure 2.** Figure 2: Relighting results from our SSD-GS pipeline. The same [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Figure 3: Shadow pipeline visualization. For each scene, we show per-ray transmittance [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

**Figure 4.** Figure 4: Qualitative comparison on real datasets from NRHints ( [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗

**Figure 5.** Figure 5: Visualization of reconstructed components under different reflectance decompositions and [PITH_FULL_IMAGE:figures/full_fig_p010_5.png] view at source ↗

**Figure 6.** Figure 6: Illustration of the progressive training schedule on the [PITH_FULL_IMAGE:figures/full_fig_p015_6.png] view at source ↗

**Figure 7.** Figure 7: Illustration of the progressive training schedule on the [PITH_FULL_IMAGE:figures/full_fig_p016_7.png] view at source ↗

**Figure 8.** Figure 8: Qualitative comparison of relighting results on novel test-time lighting from synthetic [PITH_FULL_IMAGE:figures/full_fig_p017_8.png] view at source ↗

**Figure 9.** Figure 9: Qualitative comparison on synthetic datasets from SSS-GS ( [PITH_FULL_IMAGE:figures/full_fig_p019_9.png] view at source ↗

**Figure 10.** Figure 10: Qualitative relighting results on both real and synthetic datasets. Each scene is rendered [PITH_FULL_IMAGE:figures/full_fig_p020_10.png] view at source ↗

**Figure 11.** Figure 11: Qualitative relighting comparison on the [PITH_FULL_IMAGE:figures/full_fig_p021_11.png] view at source ↗

**Figure 12.** Figure 12: Visualization of reconstructed components under different reflectance decompositions [PITH_FULL_IMAGE:figures/full_fig_p022_12.png] view at source ↗

**Figure 13.** Figure 13: Qualitative comparison for the Shadow–SSS interaction ablation study. Applying the [PITH_FULL_IMAGE:figures/full_fig_p023_13.png] view at source ↗

**Figure 14.** Figure 14: Comparison with screen-space shadow baselines. Top row: reference renderings. Second [PITH_FULL_IMAGE:figures/full_fig_p026_14.png] view at source ↗

read the original abstract

We present SSD-GS, a physically-based relighting framework built upon 3D Gaussian Splatting (3DGS) that achieves high-quality reconstruction and photorealistic relighting under novel lighting conditions. In physically-based relighting, accurately modeling light-material interactions is essential for faithful appearance reproduction. However, existing 3DGS-based relighting methods adopt coarse shading decompositions, either modeling only diffuse and specular reflections or relying on neural networks to approximate shadows and scattering. This leads to limited fidelity and poor physical interpretability, particularly for anisotropic metals and translucent materials. To address these limitations, SSD-GS decomposes reflectance into four components: diffuse, specular, shadow, and subsurface scattering. We introduce a learnable dipole-based scattering module for subsurface transport, an occlusion-aware shadow formulation that integrates visibility estimates with a refinement network, and an enhanced specular component with an anisotropic Fresnel-based model. Through progressive integration of all components during training, SSD-GS effectively disentangles lighting and material properties, even for unseen illumination conditions, as demonstrated on the challenging OLAT dataset. Experiments demonstrate superior quantitative and perceptual relighting quality compared to prior methods and pave the way for downstream tasks, including controllable light source editing and interactive scene relighting. The source code is available at: https://github.com/irisfreesiri/SSD-GS.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

SSD-GS adds a dipole scattering module, occlusion-aware shadow refinement, and anisotropic Fresnel specular to 3DGS relighting, but the abstract gives no tables, ablations, or error bars to check whether the decomposition actually separates lighting from materials.

read the letter

SSD-GS decomposes reflectance into diffuse, specular, shadow, and subsurface scattering on top of 3D Gaussian Splatting. It introduces a learnable dipole-based scattering module, an occlusion-aware shadow formulation with a refinement network, and an anisotropic Fresnel model for specular. These replace the coarser diffuse-specular splits or fully neural shading used in earlier 3DGS relighting papers. Progressive integration during training is meant to keep the components from bleeding into each other so the representation generalizes to unseen lights on the OLAT dataset. The authors report better quantitative and perceptual quality and have released the code, which is useful for anyone who wants to try the modules directly. This is a clear incremental step for people who need more physical interpretability when relighting captured scenes for VR or content tools. The abstract does not include any quantitative tables, ablation results, error bars, data-split details, or baseline comparisons, so the size of the gains and the reliability of the disentanglement are impossible to judge from what is shown. The stress-test concern holds: each module has its own learnable parameters that could absorb lighting effects rather than isolate material properties, and nothing in the abstract indicates per-component ground truth or strong physical constraints that would prevent that. Without those, the observed relighting quality on test lights could come from fitting the training illumination statistics. This paper is for graphics researchers who already work with 3DGS and want to extend it toward more accurate material handling. Readers focused on practical relighting pipelines will get concrete new components to examine or adapt. It is coherent on its own terms and targets a real gap, so it deserves a serious referee to check the experiments and clarify how much the new modules actually deliver.

Referee Report

3 major / 2 minor

Summary. The manuscript introduces SSD-GS, a relightable 3D Gaussian Splatting framework that decomposes scene reflectance into diffuse, specular, shadow, and subsurface scattering components. Key innovations include a learnable dipole-based scattering module for subsurface transport, an occlusion-aware shadow formulation with a refinement network, and an anisotropic Fresnel model for specular reflections. The method uses progressive component integration during training to disentangle lighting from material properties and demonstrates improved relighting performance on the OLAT dataset for novel lighting conditions.

Significance. If the four-component decomposition and progressive training produce a genuinely lighting-invariant material representation that generalizes to unseen illuminations, the work would advance physically interpretable relighting within 3DGS pipelines. It targets limitations of prior coarse decompositions for anisotropic metals and translucent materials, with potential downstream value for controllable light editing. The public code release supports reproducibility and extension.

major comments (3)

[Abstract] Abstract: The central claim of superior quantitative and perceptual relighting quality on the OLAT dataset is stated without reference to specific metrics (e.g., PSNR, SSIM, LPIPS), exact baselines, data splits, error bars, or ablation results. This absence prevents verification that the dipole scattering, shadow refinement, and anisotropic Fresnel modules deliver measurable gains over prior 3DGS relighting approaches.
[Training procedure] Training procedure (progressive integration section): The claim that progressive integration of the four components enforces disentanglement of lighting from material properties for completely novel illuminations is load-bearing for the generalization result. However, the dipole scattering module and shadow refinement network introduce additional learnable parameters without per-component ground truth, explicit physical regularization, or a proof that the schedule prevents lighting leakage into reflectance parameters.
[Shadow formulation] Shadow formulation: The occlusion-aware shadow model combines visibility estimates with a refinement network, yet no analysis demonstrates that this prevents shadow effects from being absorbed into the diffuse or specular components under lighting conditions absent from training.

minor comments (2)

[Abstract] The source code link is welcome; the repository should include OLAT preprocessing scripts and full training configurations to enable exact reproduction of the reported results.
[Method] Notation for the dipole-based scattering module would benefit from explicit equations defining the dipole approximation and its integration with the Gaussian splat rendering equation.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive review of our manuscript on SSD-GS. We address each major comment point by point below, with clear indications of revisions where the manuscript will be updated.

read point-by-point responses

Referee: [Abstract] Abstract: The central claim of superior quantitative and perceptual relighting quality on the OLAT dataset is stated without reference to specific metrics (e.g., PSNR, SSIM, LPIPS), exact baselines, data splits, error bars, or ablation results. This absence prevents verification that the dipole scattering, shadow refinement, and anisotropic Fresnel modules deliver measurable gains over prior 3DGS relighting approaches.

Authors: We agree that the abstract would be strengthened by including concrete quantitative details. In the revised version, we will update the abstract to explicitly reference the key metrics (PSNR, SSIM, LPIPS) from our OLAT experiments, name the primary baselines, note the data splits used, and point to the main results table and ablations that quantify the gains from each module. revision: yes
Referee: [Training procedure] Training procedure (progressive integration section): The claim that progressive integration of the four components enforces disentanglement of lighting from material properties for completely novel illuminations is load-bearing for the generalization result. However, the dipole scattering module and shadow refinement network introduce additional learnable parameters without per-component ground truth, explicit physical regularization, or a proof that the schedule prevents lighting leakage into reflectance parameters.

Authors: The progressive integration schedule is motivated by the need to stabilize learning of simpler components before adding scattering and shadow effects, which our ablations on novel OLAT illuminations show improves material consistency. While per-component ground truth is unavailable in the dataset and we do not offer a formal mathematical proof of zero leakage, the combination of sequential training and rendering losses empirically reduces lighting absorption into reflectance parameters. We will expand the training section with a clearer schedule description, additional parameter-stability ablations under unseen lights, and any applicable regularization details. revision: partial
Referee: [Shadow formulation] Shadow formulation: The occlusion-aware shadow model combines visibility estimates with a refinement network, yet no analysis demonstrates that this prevents shadow effects from being absorbed into the diffuse or specular components under lighting conditions absent from training.

Authors: We will add targeted analysis in the revised manuscript, including qualitative renderings and quantitative comparisons (with and without the refinement network) on held-out OLAT lighting conditions. These will demonstrate that the occlusion-aware formulation isolates shadow contributions rather than allowing them to leak into diffuse or specular terms. revision: yes

Circularity Check

0 steps flagged

No significant circularity; derivation relies on empirical training and evaluation

full rationale

The paper's claimed result is that a four-component decomposition (diffuse, specular with anisotropic Fresnel, shadow with visibility refinement, dipole subsurface scattering) plus progressive integration during training produces lighting-invariant materials that generalize to unseen illuminations on the OLAT dataset. This is presented as an empirical outcome of the optimization and architecture, not a definitional identity or fitted parameter renamed as prediction. No equations, self-citations, or uniqueness theorems are quoted that reduce the relighting performance to the training inputs by construction. The progressive schedule and component modules are independent design choices whose effectiveness is tested externally via novel-light relighting metrics.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 2 invented entities

Based solely on the abstract, the method relies on standard physically-based rendering assumptions and introduces learnable modules whose parameters are fitted to data; no explicit free parameters or invented physical entities are named.

free parameters (1)

learnable parameters of dipole scattering module and shadow refinement network
These are trained during the progressive integration process to fit the target appearance.

axioms (1)

domain assumption Accurate modeling of light-material interactions via diffuse, specular, shadow, and subsurface components is essential for faithful appearance reproduction under novel lighting.
Stated in the abstract as the core motivation.

invented entities (2)

dipole-based scattering module no independent evidence
purpose: Model subsurface transport in translucent materials
New learnable component introduced for scattering.
occlusion-aware shadow formulation with refinement network no independent evidence
purpose: Integrate visibility estimates for accurate shadows
New formulation combining visibility and neural refinement.

pith-pipeline@v0.9.0 · 5553 in / 1269 out tokens · 69782 ms · 2026-05-10T15:01:05.342960+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

7 extracted references · 6 canonical work pages

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Shadow-on-SSS

on the synthetic datasets provided by SSS-GS, trained for 60K iterations. The best and second- best results (based on PSNR) are highlighted in red and orange , respectively. MethodDataset Bunny Candle Dragon Soap Statue AverageTrain Test Train Test Train Test Train Test Train Test Train TestPSNR↑KiloOSF - - - - - - - - - - -25.91±1.88SSS-GS - - - - - - - ...

work page arXiv 1997