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

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

cs.CVApr 14, 2026

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

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

Organizations: Victoria University of Wellington

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.

Figures & tables

Figure 1: Overview of the proposed SSD-GS pipeline. Our method incorporates four physically inspired reflectance terms: diffuse, specular, shadow, and subsurface scattering, to model realistic light–material interactions. These components are progressively introduced during training, allowing the network to gradually disentangle complex illumination effects and improve relighting fidelity under unseen lighting conditions.

Figure 2: Relighting results from our SSD-GS pipeline. The same Bunny view under two different lighting conditions from the SSS-GS synthetic dataset ( Dihlmann et al., 2025 ) .

Figure 3: Shadow pipeline visualization. For each scene, we show per-ray transmittance $v_{i}$ , coarse visibility $\hat{v}_{g}$ , the refined shadow $S(x)$ , and the final rendered result with metrics. The progression illustrates how continuous transmittance yields smooth, geometry-aware soft shadows.

Panel of Table 1 — Table 1: Quantitative comparison results. The best/second-best results are colored in red / orange .

Figure 4: Qualitative comparison on real datasets from NRHints ( Zeng et al., 2023 ) . It presents relighting results on the test set under novel lighting. The best/second-best results (based on PSNR) are highlighted in red / orange .

Figure 5: Visualization of reconstructed components under different reflectance decompositions and training schedules on the Pixiu scene from the NRHints real dataset. Top: six reflectance compositions (Comp. A-F); Bottom: four training schedules (Sched. H-K).

Panel of Table 2 — Table 2: Ablation study results on the real scene Pixiu .

Appendix figures & tables13 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 6: Illustration of the progressive training schedule on the pixiu scene from the NRHints real dataset ( Zeng et al., 2023 ) . The model is trained for a total of 100K iterations.

Figure 7: Illustration of the progressive training schedule on the bunny scene from the SSS-GS synthetic dataset ( Dihlmann et al., 2025 ) . The model is trained for a total of 60K iterations.

Figure 8: Qualitative comparison of relighting results on novel test-time lighting from synthetic datasets provided by $\text{GS}^{3}$ ( Bi et al., 2024 ) . Note that the rendered view of RNG’s results on the Drums and Lego scenes are obtained using their official code trained using a set of white-background images.

Panel of Table 3 — Table 3: Quantitative comparison with SSS-GS ( Dihlmann et al., 2025 ) and KiloOSF ( Yu et al., 2022 ) 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.

Figure 9: Qualitative comparison on synthetic datasets from SSS-GS ( Dihlmann et al., 2025 ) . The left column shows reconstruction results on training views, while the right column presents relighting results on novel test-time lighting. The best and second-best results (based on PSNR) are highlighted in red and orange , respectively.

Figure 10: Qualitative relighting results on both real and synthetic datasets. Each scene is rendered from the same camera viewpoint under six novel lighting conditions.

Figure 11: Qualitative relighting comparison on the Translucent scene. Each row shows renderings from the same camera viewpoint under four novel lighting conditions. Our method is compared against RNG ( Fan et al., 2025 ) and GS 3 ( Bi et al., 2024 ) , demonstrating superior fidelity in reproducing light–material interactions.

Dataset		NRHints		GS 3				SSS-GS		Average
Scenes		Pixiu	Fish	Translucent	FurBall	Lego	Hotdog	Bunny	Dragon
PSNR $\uparrow$
A: Diff	Train	20.2869	23.7003	16.6152	18.5678	20.6157	18.4717	21.3548	27.7851	20.9247
A: Diff	Test	20.1878	24.6146	15.3146	17.4010	17.6430	16.4279	21.3013	27.7071	20.0747
B: D + S	Train	20.7321	24.8949	16.6980	18.3871	20.6891	18.5180	21.4677	27.9589	21.1682
B: D + S	Test	20.5692	25.3627	15.3200	17.1781	17.6290	16.4429	21.2236	27.6713	20.1746

Appendix

Table 4: Ablation study results on both real and synthetic datasets. The best/second-best results are colored in red / orange .

Figure 12: Visualization of reconstructed components under different reflectance decompositions and training schedules on the Translucent scene from the GS 3 synthetic dataset. Top: six reflectance compositions (Comp. A-F); Bottom: four training schedules (Sched. H-K).

Dataset		NRHints			GS 3				SSS-GS		Average
Scenes		Pixiu	Fish	FurScene	Translucent	FurBall	Lego	Hotdog	Bunny	Dragon
PSNR $\uparrow$
Shadow-on-SSS	Train	32.1850	31.7531	31.6750	30.8066	35.4219	31.1002	32.1094	35.9068	37.3346	33.1436
Shadow-on-SSS	Test	30.9756	31.1206	30.6575	30.0740	34.9420	30.3487	31.5272	33.6508	35.8476	32.1271
Ours	Train	33.6065	32.0748	31.7846	32.6058	35.4793	31.1434	32.4901	40.7672	39.3646	34.3685
Ours	Test	31.1213	31.1646	30.7349	32.3919	35.1639	30.4664	32.1330	37.2270	36.6325	33.0039

Appendix

Table 5: Quantitative comparison when applying the shadow term to the SSS component.

Figure 13: Qualitative comparison for the Shadow–SSS interaction ablation study. Applying the shadow term to the SSS component leads to over-darkened scattering, loss of back-lit translucency, and reduced soft shading. Our formulation preserves translucent appearance while maintaining consistent shadow behavior.

Panel of Table 6 — Table 6: Quantitative comparison results with expanded relighting baselines: TensoIR and R3DG. The best results are colored in red .

Figure 14: Comparison with screen-space shadow baselines. Top row: reference renderings. Second row: shadows produced by our method. Third and fourth rows: shadows from GS 3 ( Bi et al., 2024 ) and RNG ( Fan et al., 2025 ) , which both rely on screen-space opacity accumulation.

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We present VolS-GS, a relightable Gaussian splatting framework that reconstructs objects from one-light-at-a-time (OLAT) captures and renders them under novel lighting and viewpoints. Relightable Gaussian Splatting methods typically model appearance independently at each primitive, which makes non-local effects difficult to represent. This limitation is particularly apparent for subsurface scattering, where light entering the object at one location can emerge at another. Rather than modeling this effect solely with a neural network or a local kernel at each primitive, we use the spatial support of the Gaussian scene as the domain of a differentiable finite-volume transport solver, so that light can propagate through the object's interior. A small network predicts scattering and absorption coefficients for each Gaussian, and the solve redistributes incident light through the resulting field. The coefficients are fit to images rather than measured, so the solve supplies a transport-shaped path for aggregating per-primitive appearance, not a measurement of the material. To keep the learned shadow and specular terms from taking over the other components, our shadow term is predicted from visibility together with the transmittances and the scattering the solve produces, and a regularizer suppresses specular highlights in regions the shadow term predicts to be unlit. Experiments on three OLAT benchmarks show that VolS-GS consistently improves relighting quality on held-out lights and views.

Abstract

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