SSD-GS: Scattering and Shadow Decomposition for Relightable 3D Gaussian Splatting
Authors: Iris Zheng
Guojun Tang
Alexander Doronin
Paul D. Teal
Fang-Lue Zhang
Venue: ICLR 2026 Poster (2026)
Keywords: 3D Gaussian splatting
Relighting
Novel View Synthesis
Light Decomposition
Subsurface Scattering
Physically-Based Rendering
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: We propose SSD-GS, a physically-motivated Gaussian splatting method that decomposes light into diffuse, specular, shadow, and subsurface scattering components for realistic relighting.

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](https://github.com/irisfreesiri/SSD-GS).

Published as a conference paper at ICLR 2026
SSD-GS: SCATTERING AND SHADOW DECOMPOSI- TION FOR RELIGHTABLE 3D GAUSSIAN SPLATTING
Iris Zheng, Guojun Tang, Alexander Doronin, Paul Teal, Fang-Lue Zhang Victoria University of Wellington
OLAT Images
Ground Truth
Rendered Image
Gaussian
p- position
Z- covariance
opacity
material vector
diffuse color
specular color
Sus scatter color
Forward Splating
Subeufaace scattering
Senddure
Shadow
x(
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 material interactions. These components are progressively introduced during training, allow- ing the network gradually disentangle complex illumination effects and improve relighting fidelity under unseen lighting conditions.
ABSTRACT
We present SSD-GS, physically-based relighting framework built upon 3D Gaussian Splatting (3DGS) that achieves high-quality reconstruction and photore- alistic 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 limited fidelity and poor physical interpretability, particularly for anisotropic met- als and translucent materials. To address these limitations, SSD-GS decomposes reflectance into four components: diffuse, specular, shadow, and subsurface scat- tering. We introduce learnable dipole-based scattering module for subsurface transport, an occlusion-aware shadow formulation that integrates visibility esti- mates with a refinement network, and an enhanced specular component with an anisotropic Tresnel-based model. Through progressive integration of all compo- nents during training, SSD-GS effectively disentangles lighting and material prop- erties, for unseen illumination conditions, demonstrated the challeng- ing OLAT dataset. Experiments demonstrate superior quantitative and perceptual relighting quality compared prior methods and pave the way for downstream tasks, including controllable light source editing and interactive scene relighting. The source code is available at: http:://tthhuucc////fffsssssssi cow./riiffreeeii//SS--G.
LFn Zhang (fanglue.zhang6e www.ww.wwcmmmmmm nw. is the corresponding author

---

Published as a conference paper at ICLR 2026
INTRODUCTION
Photorealistic 3D) reconstruction with relightable capabilities has become increasingly important across domains such as AR/VR for digital humans, cinematic visual effects, cultural heritage preser- vation, and medical simulation. Traditional methods (Levoy & Hanrahan, 1996; Seitz & Dyer, 1996; 1997; Snavely et al., 2006), however, typically compromise either geometric precision or photore- alistic quality, particularly in complex lighting conditions or with reflective and textured surfaces. While these approaches enabled view synthesis under captured illumination, they relied on explicit geometric reconstructions and provided no means to disentangle reflectance from lighting. As result, they cannot support relighting under novel illumination, which is essential for realistic ap- pearance reproduction in many applications. More recently, neural rendering approaches, in partic- ular those based on Neural Radiance Fields (NeRF) (Mildenhall et al., 2020), have made notable progress by jointly encoding geometry and appearance in an implicit volumetric representation. Methods such as DNL (Gao et al., 2020) and NRHints (Zeng et al., 2023) introduce explicit lighting supervision and learnable shading representations support relightable view synthesis. However, NeRF-based methods typically suffer from high computational cost, which limits their practicality for interactive real-time applications.
3D Gaussian Splatting (3DGS), initially developed for real-time radiance field rendering, has emerged as compelling alternative NeRF-style implicit representations that rely on ray march- ing, offering superior computational efficiency and rendering fidelity. Recent extensions of 3DGS for relightable rendering fall into two main paradigms. Some methods assume static lighting con- ditions during training (Jiang et al., 2023; Liang et al., 2024; Chen et al., 2024; Gao et al., 2024), which fundamentally lacks their flexibility for photorealistic relighting. Others leverage dynamic lighting configurations such as one-light-at-a-timee (OLAT) capture setups (Bi et al., 2024; Kuang al., 2024; Fan et al., 2025; Dihlmann et al., 2025), offering more physically plausible supervision but making it difficult to disentangle material properties from illumination. This disentanglement crucial for simulating complex light transport behaviors of real-world materials, where nonlinear interactions give rise to visually critical phenomena such gradient soft shadows and subsurface scattering. Consequently, developing robust techniques to model these intricate lighting-material material interactions remains a substantial technical challenge for relightable 3D reconstruction.
We propose SSD-GS, a physically- based relighting method designed for 3DGS, where "physically- based" follows the real-time PBR convention and denotes the use of physically inspired reflectance models within an efficient rasterized framework. Built upon the 3DGS pipeline, our framework explicitly decomposes complex reflectance into four components: diffuse, specular, subsurface scat- tering, and shadow. Our main contributions are:
We introduce a learnable dipole-based scattering module that simulates realistic subsurface scattering effects using physically motivated diffusion profiles.
We design occlusion-aware shadow formulation that combines visibility prior with a learned refinement network, enabling accurate modeling view- and light-dependent shadowing effects.
We progressively integrate all reflectance components (diffuse, specular, shadow, and sub- surface scattering) during training and refine both lighting and camera conditions, leading to improved relighting quality and stronger generalization under novel illuminations.
2
RELATED WORKS
Accurate relighting and novel view synthesis require recovering both scene geometry and material appearance under illumination. We review NeRF- and 3DGS-based relighting methods, followed by subsurface scattering (SSS) models for physically plausible rendering.
NeRF-based Relighting. Neural Radiance Fields (Midenlillll et al., 2020) represent scenes as volumetric fields optimized from posed RGB images, enabling photorealistic novel view synthesis under fixed lighting. Extensions for relighting factorize appearance into reflectance and illumination using priors explicit transport modeling. For instance, (eriniSinvva et al., 202 NeRD (Boss et al., 202 1), and NeRFactor (Zhang et al., 2021b) disentangle reflectance under static lighting with geometry-aware priors, while PhySG (Zhang et al., 2021 a) uses spherical Gaussians to represent
2

---

Published as a conference paper at ICLR 2026
BRDFs and environment lighting. To address directional lighting, ReNeRF (Xu et al., 2023) models near-field OLAT illumination via a spherical codebook and light transport decoder, enabling spa- tially varying lighting. NRHints(Zeng et al., 2023) injects OLAT-derived shadow and highlight hints into a NeRF-style radiance field, achieving relighting effects comparable to DNL(Gao et al., 2020) but using a fully volumetric, single-branch design. However, NeRF-based methods suffer from im- plicit, non-physical representations, making decomposition hard to interpret or control. Moreover, they are computationally expensive, requiring hours of training per scene.
Gaussian- based Relighting, 3DGS (Kerbl et al., 2023) models scenes as point clouds of anisotropic Gaussians with learned extent, opacity, and view-dependent appearance. While efficient for view synthesis, its SH-based color encoding (Ramamoorthi & Hanrahan, 2001; Sloan et al., 2002) is inherently limited to smooth, fow-frequency angular variations, which reduces expressiveness for capturing high-frequency effects such as specular highlights and scattering. Several extensions en- hance 3DGS with physically motivated components, including GaussianShader (Jiang et al., 2023), GI-GS (Chen et al., 2024), and R3DG (Gao et al., 2024). However, these typically assume static lighting conditions, which prevents them from generalizing to novel illuminations. Their relightable variants usually perform global relighting using environmental maps, but lack the ability to model precise changes in individual light sources. To overcome these limitations, recent works exploit dy- namic lighting conditions, most notably one-light-at-a-time (OLAT) datasets. GS (Bi et al., 2024) decomposes reflectance by modeling diffuse and specular terms at the Gaussian level, while han- dling shadows and other residual effects the pixel level in deferred rendering style (Ye et al., 2024). However, this design struggles to capture complex light transport phenomena such as soft shadows and indirect illumination. OLAT Gaussians (Kuang et al., 2024) use directional encodings with two MLPs to model incident and scattering components, but their use of a proxy mesh for normal supervision makes them highly sensitive to the quality of the proxy geometry. RNG (Fan et al., 2025) achieves improved shadow quality by introducing a latent appearance code, which re- places physically meaningful shading representations and thus sacrifices interpretability. Inspired by these GLLAT-based approaches, introduce physically interpretable shading to better disentangle lighting-materie interactions and extend performance to more diverse datasets.
Final
Scattering
Shadow
Specular
Difluse
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).
Subsurface Scattering. Subsurface scattering (SSS) has been extensively studied for simulating light transport in translucent materials such skin, jade, wax, and marble. Classical approaches, including the standard dipole (Jensen et al., 2001), quantized dipole (D' Eon & Irving, 2011), and di- rectional dipole (Frisvad et al., 2014), offer efficient and realistic approximations. Extensions such as chape-adaptive dipole models (Vicini et al., 2019) and advanced BSSRDF formulations (Yan et al., 2017) further enhance accuracy and generality. More recently, subsurface scattering has been explored in neural rendering frameworks through learning-basee techniques. Neural SSS (Tg et al., 2024) approximates the translucent appearance using per-view and per-light neural reflectance fields, but it relies heavily on dense supervision and lacks physical interpretability. In the context of Gaus- sian Splatting, SSS-GS (Dihlmann et al., 2025) directly learns the subsurface scattering radiance via neural network conditioned on Gaussian and lighting inputs. The output is blended with BRDF shading using a learned weight, treating SSS as a residual term rather than a physically motivated subsurface model. In contrast, integrate physically grounded subsurface scattering approach
3

---

Published as a conference paper at ICLR 2026
into the 3DGS pipeline, based on the standard dipole diffusion approximation (Jensen al., 2001). This classical method provides a closed-form BSSRDF that approximates multiple scattering in ho- mogencous media. By embedding it into the Gaussian Splatting framework, enable efficient, interpretable simulation of soft scattering effects, while maintaining modular compatibility with other shading components such as diffuse, specular, and shadow terms.
PRELIMINARY
Our method builds on the 3D Gaussian Splatting (3DGS) framework (Kerbl al., 2023), which represents scene as a set of anisotropic 3D Gaussians. Each Gaussian is defined by its center X;, opacity ag,, and a covariance matrix Σ. The covariance matrix is parameterized via a rotation matrix R; and a scaling matrix S,, such that E; R,S.S! R2 During rendering, the Gaussians are projected onto the image plane and composited using front-to-bace alpha blending as:
i-1 T, = II a,)
pixxel Σn
T. a4 C.,
j=1
Each Gaussian color C; is computed using a view-dependent SH expansion:
C,(v) Σ c.b Yo(v)
(2)
While effective for encoding smooth appearance, this SH-based model lacks physical grounding and struggles to capture high-frequency view-dependent effects. this work, we replace with a decomposed physically-bas based model to better capture full-frequency light-material interactions.
METHODOLOGY
We extend the 3D Gaussian Splatting (3DGS) framework by incorporating physically-based flectance model that replaces its original spherical harmonics (SH)-based appearance representa- tion. Our formulation decomposes shading into four components-difitee subsurface scattering (SSS) modeled either analytically or using lightweight neural fields. These components are evaluated per- Ber-Gaussian and composited form the final image, enabling interpretable supervision and relightable rendering under novel illumination. Their visual effects are illustrated Fig. 2, and a detailed ablation study is provided in Sec. 5.3. An overview of the formulation is illustrated in Fig. 1.
specular, shadow, and
4.1 PBR-BASED SHADING
We formulate a physically-basee shading function that operates directly on the 3D Gaussian rep- resentation. Unlike prior work that employs view-dependent spherical harmonics (SH) for color synthesis (Kerbl et al., 2023), we decompose reflectance into physically interpretable terms, en- abling improved photorealism, per-component supervision, and controllable relighting. The color of each Gaussian is computed
C; -cafa cafa) S(x) + Csaafses
(3)
where: fas fsr fess denote the scalar reflectance intensities for diffuse, specular, and subsurface scattering, defined in Eqs. 9, 10, and 5, respectively; Ca2 Cs, Cass E 3 are the corresponding learned base colors for each reflectance term; S(x) denotes the soft shadow factor, computed weighted average over shadow rays and further refined using an MLP, with its detailed formulation given in Eq. 8. This decomposition is evaluated per Gaussian and composited through the 3DGS forward-rendering rendering pipeline, where alpha blending accumulates Gaussian contributions into the final pixel color. The resulting image is supervised with pixel-we wise loss against the ground truth. A detailed analysis of the interaction between the shadow term and subsurface scattering is provided in A Appendix F.2.
density-
4

---

Published as a conference paper at ICLR 2026
4.2 SUBSURFACE SCATTERING TERM
We model subsurface scattering (SSS) using the standard dipole diffusion profile, with scattering properties defined per Gaussian. To predict these parameters, we train neural field Osss that maps spatial and directional inputs to the corresponding scattering coefficients.
(4) where denotes the Gaussian center, wi and wa the light and view directions, is the surface normal derived from its local z-axis, and m E IR' is a learnable per-Gaussian material embedding; aa Ja. and r denote the scattering coefficient, absorption coefficient, and surface separation distance used in the dipole formulation.
fa., Osss Wo. Wi, m)
The subsurface scattering (SSS) predictor is implemented 6-layer MLP with hidden size of 256 and ReLU activations. It takes as input the positional encodings (with L = 4 frequency bands) of the spatial location x, the viewing direction Wo, and the lighting direction W/, as well the local surface normal and a per- Gaussian material embedding m. To ensure physical plausibility and improve training stability, the network outputs are passed through sigmoid activations and rescaled to fall within plausible material-specific ranges: os, Oa E [0.05, 2.05], and r [0.1,3.1]. We evaluate the standard dipole diffusion profile (Jensen et al., 2001) as:
(ass(r) a' ( +
aodd d3
+ (o,d
d3
where a' 0,40, determined by the optical parameters (o., Ta 17). and d,,, de are the corresponding distances from the shading point to the real and virtual dipole sources, computed from the surface separation r.
+ Ja, 2, and 2e are the depths of the real and virtual dipole sources,
Our SSS formulation combines physically grounded modeling with learnable parameter prediction, enabling realistic reproduction of subsurface scattering effects without requiring external geome- try (Kuang al., 2024). Because surface normals and material properties inferred directly from the Gaussian representation, the system remains robust under challenging geometric conditions. As result, it generalizes well complex noisy regions where mesh-derived normals may be unre- liable, thus preserving effective scattering estimation.
4.3 SHADOW TERM
We model soft shadows using a two-stage approach that combines per-ray shadow evaluation with neural refinement. In the first stage, for each Gaussian we trace a shadow ray from the light source to every pixel and accumulate transmittance into visibility cues. the second stage, a compact neural module takes these cues, together with geometry and material features, and predicts a scalar decay factor used in shading.
Stage 1: Shadow Evaluation. Given light direction wi. each Gaussian considers the set of pixels covered by its 2D projection. For each pixel, we evaluate a shadow ray from the light source toward that pixel and accumulate the opacity of intervening Gaussians. This yields continuous per-ray transmittance
V/3 II (1-ax), REO,
(6)
where O, is the depth-ordered set of Gaussians intersected by the shadow ray, and ag [0, denotes the opacity of Gaussian k.
To obtain soft shadow estimate, these per-ray transmittance values aggregated using the Gaus- sian's 's projected density as weights. Let μ; denote the projected density of Gaussian at pixel i. The coarse visibility of Gaussian g then defined as the deishty- expectation,
C.P49 Σ.p
0g
which summarizes how much light from the direction W; reaches the Gaussian after accounting for overlapping geometry, and serves compact visibility estimate.
5

---

Published as a conference paper at ICLR 2026
Stage 2: Neural Refinement. The coarse visibility 0 captures the primary directional shadowing trend but may miss fine variations arising from contact shadows, geometric details, and material- dependent attenuation. To account for these effects, we refine é using a lightweight neural mod- ule shud, which predicts a shadow attenuation term as a function position:
S(x) = Shad 0, Wi, m)
The shadow refinement network implemented using 3-layer MLP with 32 hidden units per layer and ReLU activations. Its inputs include the Gaussian center incident light direction wi, coarse shadow estimate é, and material embedding m. To capture high-frequence spatial and directional variation, both and
encoded using positional encoding with L 3 frequency levels.
The refined shadow term S(x) modulates the diffuse and specular components of our shading model, while the scattering tenm is added separately. This produces the final illumination contribution for Gaussian as defined Eq. 3.
Per-ray Transmittance
Coarse Visibility E
Refined Shadow
Final
Loco, ac)
((x) Ostaad (x 6, m)
Bunny
40.7321 0.9924 0.0154
Lego
34.5368 0.9832 0.0210
FurBall
33.5024 0.97791 0.0315
Translucent
Figure 3: Shadow pipeline visualization. For each scene, show per-ray transmittance U, coarse visibility Eg. the refined shadow S(x), and the final rendered result with metrics. The progression illustrates how continuous transmittance yields smooth, geometry-aware soft shadows.
A visual illustration of this progression from per-ray transmittance to coarse visibility ig. and finally to the refined shadow S(x) is provided in Fig. 3. The figure highlights how continuous volumetric visibility naturally produces smooth, ccometry-consistent soft shadows under point-light illumination. complementary comparison against screen-space shadow accumulation methods included in Appendix G.2.

---

Published as a conference paper at ICLR 2026
4.4 DIFFUSE AND SPECULAR TERMS
We decompose direct shading into diffuse and specular components. The diffuse term is mod- eled with Lambertian BRDE, which assumes uniform surface reflectance and produces view- independent, cosine-weighted reflection. Although simple, this model provides stable foundation for capturing fowffrqqenncy appearance and ensures physically meaningful supervision during the early stages of training. The specular term, in contrast, accounts for high-frequency, view-dependent reflections. We represent it as Fresnel factor (Schlick, 1994) modulated by anisotropic spherical Gaussian (ASG) bases (Xu et al., 2013). The Fresnel term captures the angular dependence of re- flection intensity, particularly the sharp increase near grazing angles, while the ASG bases provide compact yet expressive representation of anisotropic highlights. This formulation allows us to produce complex effects such as brushed metals and fabrics. Further technical details and equations provided in Appendix B.
4.5 TRAINING METHODOLOGY
To stabilize convergence and reduce interference between reflectance components, we adopt pro- gressive training strategy. Four components are introduced coarse-to-fine order across simple phases defined by small set of iteration thresholds (see Appendix A.I, Fig. 6 and Fig. 7 for details). A single default configuration is used for all scenes, ensuring that the approach remains stable and reproducible. Concurrently,
refine camera poses and lighting positions throughout training. The camera adjustment module is activated once the shadow term is introduced, while lighting position refinement begins during the specular phase. Experimental results are presented in our ablation study (see Sec. 5.3 and Appendix E.
5 EXPERIMENTS
We evaluate our relightable rendering method on both real-captured and synthetic OLAT datasets. This section first introduces the evaluated methods and datasets, followed by quantitative and quali- tative comparisons. We then present ablation studies to assess the contribution of individual model components and training strategies. All experiments are conducted on a workstation equipped with an NVIDIA RTX 3090 GPU and an Intel Core i7- 17-147 CPU, running Windows 11 Education.
5.1 DATASETS
The OLAT datasets provide controlled illumination by sequentially activating individual point light sources, and are widely used benchmarks for evaluating relightable view synthesis. To ensure consistent and challenging setup, test-time lighting directions are excluded from training.
Real Dataset. We use the seven OLAT-captured dataset provided by NRHints (Zeng et al., 2023): Cat, CatSmall, CupFabric, Fish, FurScene, Pikachu, and Pixiu. Each scene contains 500-1500 training images and 45-200 test views, all rendered against black background. CatSmall, CupFab- ric, and Pikachu are rendered at resolution of 1024x 1024, while the remaining four use 512x512.
Synthetic Datasets. We use the six synthetic scenes released by GS (Bi et al., 2024): Translucent, AnisoMetal, Drums, FurBall, Hotdog, and Lego. Each scene includes 2000 training images and 400 testing images resolution of 512x512, rendered against white background. In addition, evaluate on the five synthetic scenes from SSS-GS (Dihlmann et al., 2025): Bunny, Candle, Dragon, Soap, and Statue, which emphasize subsurface scattering effects. Each scene includes 500 training images and 500 test views at a downscaled resolution of 256 226 against a black background.
5.2 QUANTITATIVE AND QUALITATIVE ANALYSIS
First, we evaluate both reconstruction quality on the training set and relighting performance on the test under unseen lighting conditions.
We then compare our method against four representative Gaussian Splatting-basee approaches: vanilla 3DGS (baseline), GI-GS (Chen et al., 2024) representative of relighting under static

---

Published as a conference paper at ICLR 2026
Table 1: Quantitative comparison results. The best/second -best results are colored in red / orange
(a) Comparison with the original 3DGS (Kerhl et al., 2023), GI-GS (Chen et al., 2024), GS* (Bi et al., 2024), and RNG (Fan et al., 2025) on the real datasets from NRHints (Zeng et al., 2023).
Uaineet
CaiSmall
Captahnc
Fich
Turccene
Pikacha
Przu
Method
Train
Text
Tram
Text
Thin
Train
Test
Train
Text
Train
Test
Trmi
Text
PSNR
3DGS
15:2225
14.5325
22.8367
22.5727
24.9219
25.0335
2233237
223311
15.7745
13,4335
1938233
19,2310
2000114
18,5501
Gll GS
14.5255
13,9989
22. 5665
22.3223
24.0188
24. 24.3821
22.2452
22.7500
17.9692
17.8520
19,2010
19.1857
19,0030
18.1054
300055
27,4081
34,8341
34.31.3
36 16.3050
36. 16.1575
31,3205
30.2318
28.6520
28. 2222
30.0045
29.4128
30.6831
29,7001
KNG
27.1478
26. 0259
7398
14:3
32.790
37:3219
79
29.0835
27.987
27 27.0990
31.8145
31 31.2646
29. 86.0
28.8554
Ours
07.8844
18.0656
37.4702
12.0748
31.7646
70.7149
32.4506
11.9978
33,0065
31.1219
SSTNT
3DGS
07130
0.8862
039391
0,33995
0.330
0,3430
0.3424
038312
0.7988
0.7569
009633
0,9000
03822
0.8298
Gli GS
0.3310
0.3162
0,8765
0.8750
0.91.5
0.9178
0.74.00
0.2637
0.5918
0.5811
08708
0.8724
0.6229
0.5117
09340
0.9028
0.977
0.9259
0.9825
0.98211
0.9900
0.9299
09926
09168
0.621
0.9605
09957
0.9394
RNG
08556
0.8421
0.9789
0.5667
0.9801
0.9297
0.3900
0.8933
09195
09:49
0.675
0.9611
0.924
0.9187
Out
09334
0.9027
0.9785
0:9767
0.9890
0.9833
0.9963
0.9360
0.9576
0.918
0.9655
0,9637
0.9544
0.9452
LPIPS
30GS
02583
0,3033
0.1139
0.11K
0.0894
00838
011700
0.1814
0.201
01210
01116
0.1003
01473
0.1221
GB-GS
0.3496
0.3518
0.1310
0.13%
0.1151
0.1116
0.19
0.2063
0,2000
1.292
0.131
0.136
0.286
0.9089
01228
0.1338
0.0534
0.0699
0.080
0.0000
0.0816
0.0100
0.778
09007
0.711
0.0717
0.0795
0.0826
RNG
0.2021
0.0585
0.0619
0.0971
0.0076
0.1250
1.804
0.8007
0.0514
0.1057
Ours
01125)
0.1357
0.0621
0.065
0.0503
0.0006
0.0779
0.8855
0.0600
0.0744
0.0679
0.0679
0.0751
0.0791
(b) Comparison with 3DGS (Kerbl et al., 2023), GI-GS (Chen et al., 2024), GS (Bi et al., 2024), and RNG (Fan
et al., 2025) on the GS synthetic datasets.
Dataset
Translncent
MatseMetal
Drums
Hosdog
Legg
Method
Train
Tost
Train
Test
Frain
Test
Frain
Text
Frain
Test
Train
Test
PSNK
3DGG
17.1853
16.4899
18.1692
17,1009
26-5180
24.5092
21:5473
20.1206
19.3050
16.9535
19,0612
15 15.9886
GLOGS
17.1222
16.0706
17.3309
15.9567
26.7554
24.6177
21,3338
19.5295
19.1535
16.8118
19,5919
G83
51.1327
32. 1999
50.1878
28.8119
34,0111
33,2688
34.6201
34,9845
32.1779
32,7244
31 31.2224
30.5617
RNG
28.1919
28. 56.9
26.4611
25.9203
20,4970
20.3033
24.5084
23.4342
29.4099
29.5277
18.5810
18.4872
Ours
32,6058
32,3919
33,5514
3544733
35.1639
32,4901
32,1330
31.1434
30,4664
SSTM
3DGS
0.8984
0.8038
0.8998
0.8949
0.356
0.9439
03999
0.8981
0.8956
0.8399
03314
0.7904
GLGG
0.8651
0,8586
0.8537
0.8304
0,9066
0.8941
0,8230
0.8592
08636
0.8282
0.8128
0.7647
GS!
0.9787
0.9775
0,9702
0.9355
0.965
0.0841
00747
00207
09764
0.745
0.704
09581
RNG
0.9586
0.9598
0.9440
0.9393
0.9199
0.9244
0.9277
0.2204
096088
0,9572
0.776
0.8616
Ours
0,9835
0,9823
0.9762
0.9698
0.9870
0.9848
0.9776
0,933
09776
039743
0.9706
0.970
3DGS
0.0755
0.0748
0.0634
0.0704
0.0371
0.0442
0.918
0,0865
00882
0.1128
0.110
0.1416
GS
0.1137
0.1155
0.1084
0.1179
0.1142
0.1242
01843
0,1190
0,3368
0.1638
0,1458
0,1643
GS
0.0247
0.0254
0,0804
0.0341
0,0145
0.0160
0.0666
01824
00397
0,0305
0.323
0,0401
0.0438
0.0491
0.0685
0.1290
0,1264
00475
0.0484
0.1419
Ours
0.0201
0,0200
0.0255
0.0295
0.0144
0.0157
0.0482
0.0442
0.0293
0.0326
0.0331
0,0419
(c) Comparison with SSS-GS (Dihlmann et al., 2025) and KiloOSF (Yu et al.. 2022) on the SSS-GS synthetic datasets. For baselines, report the average results directly from the respective papers. while the per-scene results of our method are provided in the Tab.
Dataset
Train (Average)
Test (Average)
Other Metrics
Method
PSNR
SSIM
LPIPS
PSNK
SSIM 1
LPISS.
FPS
Train I.
GPU
KlloOSF
25.91 = 1.88
0.33 = 0.02
01197
0.03
14.4
> 20h
RTX 4090
SSS BSS-GS
35.01 1.01
0.972 0.01
0.040 0.01
1548
28.26
Ours (w/o Opt)
40,7087
0.9907
0.0123
37.4409
0.9843
0.0186
66.28
= 11.37
<. 2h
RTX 3090
Ours Opt)
41.8705
0,9924
0.0099
38.3542
0.9863
0.0158
61.50
16.23
2.5h
RTX 3090
illumination, and GS (Bi al., 2024) and RNG (Fan et al., 2025) as representatives of OLATT-based relighting. All methods are trained for 100K iterations with identical settings, and experiments are conducted on both the NRHints real dataset and the GS synthetic dataset for fair comparison.
Finally, to further validate the effectiveness of our physically based SSS shading term, we compare against SSS-GS (Dihlmann et al., 2025) and KiloOSF (Yu et al., 2022) on the SSS-GS synthetic dataset, using the quantitative results reported in the SSS-GS paper. Following the experimental setup in (Dihlmann et al., 2025), our method is trained for 60K iterations and rendered on a black background to ensure comparability.
Quantitative Results. As shown in Tab. 1, our method achieves consistently strong performance across both training and test sets. By introducing a physically based decomposition of shading terms, our approach yields clear numerical advantages on datasets with pronounced scattering and specular effects, while achieving comparable results to other relighting methods on datasets dominated by low-frequency appearance, demonstrating strong generalization across diverse scenarios.
Qualitative Results. Fig. 4 presents visual comparisons on the real-world scenes, while addi- tional results on synthetic datasets are provided in Appendix C.1 and C.2. Compared to existing
8

---

Published as a conference paper at ICLR 2026
Ours
RNG
GI-GS
3DGS
FurScene
PSNR PSNRR|SSIM| SSIM LPIPS
32.921910 | 10,0536
29.3079 1000
10,0824
2041 9445)
00025|00.00000 0.0581
10.5915 0..204410222111 00.2126
18 19.3601 0,0052 1O, 1700
Pixiu
PSNR|SS/M| |S/M| 0.9513 00.0891
0.99199
0 0.99440
0.0004
0.,8902 0.2774
Gat
PENRI 30.4543 25.0642 00.2381
15.57899
Fish
PENR| PENR|SSMM| LPIME
32 9460 0.8417 0.077! 28,1991 0,,5000 D.1184
31.0018 [0.9378 0.0805
22.8833 0.7289) 0.1912
23 23.0381| 0.8428 C.1669
Figure 4: Qualitative comparison on real datasets from NRHints (Zeng et al., 2023). It presents relighting results the test set under novel lighting, The best/second-be results (based on PSNR) are highlighted in red orange
approaches, our method produces relighting results that are consistently more faithful to the ground truth, especially in challenging scenes with complex material properties and light-material interac- tions. In particular, GS often fails to capture sharp shadow boundaries and tends to introduce noise in shadow regions, notably in scenes such Fish and FurBall. RNG, while capturing reasonable global appearance, frequently loses fine-scale reflectance and geometry details. For instance. the cat's nose is reconstructed as a flat white region instead of retaining its pink tone and curvature, and specular floor textures in the Fish scene are oversmoothed under strong lighting. These qualitative differences demonstrate our model's ability to preserve both soft shading and high-frequency details and its robust generalization to unseen lighting.
5.3 ABLATION STUDY
Reflectance Components. We evaluate different combinations of reflectance terms to understand their individual and eumulative contributions. Specifically, compare: (A) Diffuse only, serving as baseline; (B) adding specular: (C) adding subsurface scattering; and (D) the full model with all terms. We also examine ablations from the full model by removing: (E) the specular term, or (F) the scattering term. The full model (D) achieves the best overall performance. Subtractive ablations confirm these trends: removing either specular (E) scattering (F) leads noticeable degradation.
Training Schedule. We examine alternative strategies for introducing reflectance components dur- ing training: (H) joint training of all terms from the start; (I) our progressive schedule (Diffuse + Shadow -> Scatter - Specular); (J) non-physical variant swapping the last two; and (K) variant that adds all terms together after a diffuse-only warm-up. The results demonstrate the effectiveness of our progressive strategy (I), which yields superior reconstruction of reflectance components.
While the quantitative results across different compositions and training schedules remain relatively close (see Tab. 2 and the additional scenes in Tab. 4), the visual decompositions (see Fig. 5 and

---

Published as a conference paper at ICLR 2026
Difluse
Specular
Scaltering
Shadow
Final
Difluse
Specular
Scattering
Shadow
Final
Comp, Camp B
Camp
Comp, C
Comp. E
Comp,
Schod,
Sched.
Schred
Sched.
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 composi- tions (Comp. A-F); Bottom: four training schedules (Sched. H-K).
Table Ablation study results on the real scene Pixiu.
Dataset
Train
Test 1
Method
PSNR
LPIPS
A: Diff
20.2869
0.5701
0.1055
20.1878
0.5583
0.1061
B: D
20.7321
0.6800
0.0966
20.5692
0.6683
0.0992
C: D S SSS
25.1545
0.9336
0.0825
24.8100
0.9274
0.0857
D: Full (Ours)
33.6065
0.9524
0.0751
31,1213
0.9452
0.0791
E: Full - S
32.3487
0.9489
0.0817
30,5952
0.9429
0.0844
F: Full - SSS
31,5332
0.7318
0.0818
30.4095
0.7204
0.0850
H:
32.5452
0.9500
0.0781
31,0880
0.9441
0.0812
Prog. Phys (Ours)
33.6065
0.9524
0.0751
31,1213
0.9452
0.0791
J: Prog. NonPhys
32.5606
0.9499
0.0776
31.0973
0.9443
0.0807
K: Prog. Merge
33.3438
0,9520
0.0758
31,0486
0.9449
0.0794
Fig. 12) show meaningful differences that highlight the importance of proper terms and training strategies. For example, in Composition F, removing the scattering term leads to noticeable arti- facts, where both the diffuse and shadow components begin to absorb scattering, resulting a more translucent appearance that compromises the sharpness of shadows. Similarly, in Schedule K, in- troducing multiple reflectance terms simultaneously causes training interference, where overlapping gradients between specular and scattering degrade the disentanglement quality. These artifacts are less evident in scalar metrics but manifest clearly in the visual outputs, underscoring the need for structured supervision and progressive learning.
6 CONCLUSION
We demonstrate that progressively introducing reflectance terms via carefully designed training schedule enables our method to decompose scene illumination effectively and support relighting under novel lighting. Although we do not explicitly model multi-bounce global illumination. the combination of continuous volumetric visibility and the learned scattering term already captures the most perceptually important low-frequency indirect effects. While our current implementation relies on rasterization-base pipeline, which does not fully capture physical light transport, fu- ture work could integrate ray or path tracing to improve physical realism. Incorporating additional supervision, such multi-term losses, may further reduce role leakage and improve disentangle- ment of reflectance components. Material-awan aware grouping Gaussians using the learned material structured representation, facilitating controllable relighting and
latent space could produce scene editing. Overall, our work establishes a solid foundation for physically grounded, editable relightable rendering.
10

---

Published as a conference paper at ICLR 2026
Ethics Statement. This work introduces a physically based relightable 3D reconstruction frame- work that recovers geometry and appearance from sparse image observations. The method aims to preserve the visual fidelity of captured scenes and does not infer personal identity or generate content beyond illumination variation. As with other reconstruction techniques, it may be applied to data containing human subjects proprietary objects, and thus should be used in accordance with relevant consent, privacy, and intellectual property regulations.
Reproducibility Statement. We provide sufficient information in the main paper (Sec. 5) and the appendix (Sec. A and Sec. B) to support reproducibility, including details on the model design, training procedure, and evaluation setup. All datasets used for training and testing are described accordingly. We are happy to clarify any additional implementation details if needed.
Acknowledgements. This work was supported in part by the Marsden Fund Council managed by the Royal Society of New Zealand under Grant MFP--20-UU--I and internal research grant (Project No. 400876) from Victoria University of Wellington.
REFERENCES
Zoubin Bi, Yixin Zeng, Chong Zeng, Fan Pei, Xiang Feng, Kun Zhou, and Hongzhi Wu. GS Efficient Relighting with Triple Gaussian Splatting, SIGGRAPH Asia 2024 Conference Papers, pp. 1-12, December 2024. doi: 10.1145/3680528.66887 3687576. URL https::////c.. //l acm, org/
doi/10. 11..11145338000 Conference Papers ISBN: 7798400711312
3687576. Conference Name: SA '24: SIGGRAPH Asia 2024 Place: Tokyo Japan Publisher: ACM.
Mark Boss, Raphael Braun, Varun Jampani, Jonathan T. Barron, Ce Liu, and Hendrik P.A. Lensch. NeRD: Neural Reflectance Decomposition from Image Collections. In 2021 IEEE/CVF Interna- tional Conference on Computer Vision (ICCV), pp. 12664-12674, Montreal, QC. Canada, October 2021. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). ISBN 978-1-6654 2812-5. doi: 10.1 document/97100 9710085//
.01245. URL https: eeeepeeellooo ore.. ore. ieee org/
Hongze Chen, Zehong Lin, and Jun Zhang. GI-GS: Global Illumination Decomposition on Gaussian Splatting for Inverse Rendering. ICLR 2025, October 2024. URL https: /ttp////eeeee...e.////e net
iiniihhiE JIvhL.
Eugene Eono and Geoffrey Irving. Quantized-Diffision Model for Rendering Translucent Ma- terials. ACM Transactions on Graphics, 30(4):1-14, July 2011. ISSN 0730-0301, 1557-7368. doi: 10.114572010324.19699555 URL https::/// //dl. acm. org/ /oi/ /1/ 114.0.111112222022311 1964951.
Jan-Niklas Dihlmann, Arjun Majumdar, Andreas Engelhardt, Raphael Braun, and Hendrik P.A. Lensch. Subsurface Scattering for 3D Gaussian Splatting. Proceedings of the 38th Inter- national Conference on Neural Information Processing Systems, volume 37 of NIPS '24, pp. 121765-121789, Red Hook, NY, USA, June 2025. Curran Associates Inc. ISBN 979-8-3313- 1438-5.
Jiahui Fan, Fujun Luan, Jian Yang, Miloš Hašan, and Beibei Wang. RNG: Relightable Neural Gaus- sians. 2025 IEEE/CVF Conference Computer Vision and Pattem Recognition (CVPR), April 2025. doi: 10. 2aSS550/rXii..20011990 PXXi:2440.119700 [es].
URL http:// / //txxvviivv/////////////////aavl http://aaxvv.og........
/
19702.
Jeppe Revall Frisvad, Toshiya Hachisuka, and Thomas Kim Kjeldsen. Directional Dipole Model for Subsurface Scattering. ACM Transactions Graphics, 34(1):1-12, December 2014. ISSN 0730- 0301, 1557-7368.
doi: 10. 10.1145/2682629. URL httph:tt///// /dl. amc. ort/ /tt.//////11.1/1//////. 1145/
2682629.
Duan Gao, Guojun Chen, Yue Dong, Pieter Peers, Kun Xu, and Xin Tong. Deferred Neural Lighting: Free-viewpoin Relighting From Unstructured Photographs. ACM Trans. Graph., 39(6):58:11 258: 15, November 2020. ISSN 0730-0301. doi: 10. 10.14447/41..8..177777 URL https:
//dl cm. wwtp/oo////o../oooooooo /ttg:///o//.1111/444455555555550

---

Published as a conference paper at ICLR 2026
Jian Gao, Chun Gu, Youtian Lin, Zhihao Li, Hao Zhu, Xun Cao, Li Zhang, and Yao Yao. Relightable 3D Gaussians: Realistic Point Cloud Relighting with BRDF Decomposition and Ray Tracing. In Computer Vision - ECCV 2024: 18th European Conference, Milan, Italy, September 29 29-October 4, 2024, Proceedings, Part XLV, pp. 73-89, Berlin, Heidelberg, November 2024. Springer- Verlag. ISBN 978-3-031-7299422 doi: 10.
URL https: //o. org/
10. 10.1007/9783
72995- 9_5.
Chun Gu, Xiaofei Wei, Zixuan Zeng, Yuxuan Yao, and Li Zhang. IRGS: Inter-Reflective Gaussian Splatting with 2D Gaussian Ray Tracing. In 2025 IEEE/CVF Confer- ence Computer Vision and Putlern Recognition (CVPR), pp. 10943-10952, 2025.
URL https:
IRGS_ https://opente /pppappppccccssccaeeaaaeeae..eae theccf. Gaussian SeauussinSSplattinng Splatting with 2D Gaussian Ray_ Tracing_cVPR_20222 wwwp_ccrr222___pperr.... html.
Henrik Wann Jensen, Stephen R. Marschner, Marc Levoy, and Pat Hanrahan. A Practical Model for Subsurface Light Transport. In Proceedings of the 28th annual conference on Computer graphics and interactive techniques, pp. 511-518. ACM, August 2001. ISBN 978- 978-1-58113--7444 doi: 10. 1145/383259.3833 11449/883259.333399 URL https:ttp...... / /dl. a acm. www//oii/////ooooooooo 10. / / 383259..299. 383319.
Yingwenqi Jiang, Jiadong Tu, Yuan Liu, Xifeng Gao, Xiaoxiao Long, Wenping Wang. and Yucxin Ma. GaussianShader: 3D Gaussian Splatting with Shading Functions for Reflective Sur- faces. In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5322-5332, November 2023. doi: 10.1109/CVPR52233.2000 202.4.003...02.00000. URL https: //ieeexplore. ieee. org ottp///ccceett//05555/////arrrre 2575-7075.
ISSN:
Haian Jin, Isabella Liu, Peijia Xu, Xiaoshuai Zhang, Songfang Han, Sai Bi, Xiaowei Zhou, Zexiang Xu, and Hao Su. TensolR: Tensorial Inverse Rendering. In 2023
IEEE/CVF Conference on Computer Vision and Puttern Recognition (CVPR), pp. 165-174,
2023. URL htttps:://penacceeet www.cheec...cco..om.. wwtp/////oo//////////// http//////tmmm///eeee htthhht//// Jin
paper.ppp... html.
Bernhard Kerbl, Georgios Kopanas, Thomas Leimkuehler, and George Drettakis. 3D Gaussian Splatting for Real-Time Radiance Field Rendering. ACM transactions on graphics, 42(4))1-14, 2023. ISSN 0730-0301. doi: 10. 145/3592433.
Place: New York, NY, USA Publisher: ACM.
Zhiyi Kuang, Yanchao Yang, Siyan Dong, Jiayue Ma, Hongbo Fu, and Youyi Zheng. OLAT Gaussians for Generic Relightable Appearance Acquisition. SIGGRAPH Asia 2024 Con- ference Papers, SA '24 pp. 1-11, New York, NY, USA, December 2024. Association for Computing Machinery. ISBN 979-8-4007-1131-2
doi: 10.1145/3680558..6676666 https: acm. otg/ / /10. (1l.111556680228836388888 3687677
URL
Mare Levoy and Pat Hanrahan. Light Field Rendering. In Proceedings the 23rd annual conference on Computer graphics and interactive techniques, SIGGRAPH '96, pp. 31-42, New York, NY, USA, August 1996. Association for Computing Machinery. ISBN 978 978-0-89791-746-99 doi: 10.
1145/237170.237199 URL https: h/t///ll./// acm. org/ doi 10. 10....11//211100000
Zhihao Liang, Qi Zhang, Ying Feng. Ying Shan, and Kui Jia. GS-IR: 3D Gaussian Splatting for Inverse Rendering. 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition
(CVPR), pp. 21644-21653, June 2024. doi: 10.1 10.1109/VVPS5273..000 2.2733..022.02045. URL https: / ieeexplore ieee...... org/ /oc.menntttttceee / 1111116666666 64/. Conference Name: 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) ISBN: 9798350353006 Place: Seattle, WA, USA Publisher: IEEE.
Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, and Ren Ng. NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis. In Andrea Vedaldi. Horst Bischof, Thomas Brox, and Jan-Michael Frahm (eds.), Computer Vision ECCV 2020, pp. 405-421, Cham, 2020. Springer International Publishing. ISBN 978-3-030-58452-88 doi: 1..10077978-3-0280 3-000-5445-55 24.
12

---

Published as a conference paper at ICLR 2026
Ravi Ramamoorthi and Pat Hanrahan. An Efficient Representation for Irradiance Environment Maps. In Proceedings of the 28th annual conference on Computer graphics and interactive tech-
niques, pp. 497-500. ACM, August 2001. ISBN 978- 978-1-58113-374-55 doi: 10.1145/383259. 383317. URL https://///o / /1l acm org/ do// 11e.11114332555 3833177
Christophe Schlick. An Inexpensive BRDF Model for Physically-based Rendering. Com- puter Graphics Forum, 13(3):33-246, 1994. ISSN 1467-8659.
doi: 10.1111/
1467-8659 1467-8659.1330333. 1330233.
URL https:/ /on1 iine weeeeerrrrry.. wi ley cwm/ abs//abbs/1.
1111/1467-88 1330033, _eprint: http:///woiiiiaaaageieee/////eea
Steven M. Seitz and Charles R. Dyer. View Morphing. In Proceedings of the 23rd amnual conference on Computer graphics and interactive techniques, SIGGRAPH '96, pp. 21-30, New York, NY, USA, August 1996. Association for Computing Machinery. ISBN 978-0-89791-746-9, doi: 10. 1145/237/70.2377999 URL https: acm..acc.oom /tt.:////.......1111112111111111
Steven M. Seitz and Charles R. Dyer. Photorealistic Scene Reconstruction by Voxel Coloring. In Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97), CVPR '97, pp. 1067, USA, June 1997. IEEE Computer Society. ISBN
Peter-Pike Sloan, Jan Kautz, and John Snyder. Precomputed Radiance Transfer for Real-Time Ren- dering in Dynamic, Low-Frequency Lighting Environments. ACM Transactions on Graphics 21(3)522-5336, July 2002. ISSN 0730-0301, 1557-7368. hitpss:/ddl acm. http:////..........//////000000 01610/556...555555...
doi: 10. 00.114455666544566612 URL,
Noah Snavely, Steven M. Seitz, and Richard Szeliski. Photo Tourism: Exploring Photo Collections in 3D. In ACM SIGGRAPH 2006 Papers, SIGGRAPH '06, pp. 835-846, New York, NY, USA, July 2006. Association for Computing Machinery. ISBN 978-1-59593-33645.
doi: 10.1145/
1179352. 19..1114111 141964. URL https: att.ttppmdd...am..mmm.. wtw.aaam.oo...ooo..ooooo/// wwtp:/o/g/////////o/oo/ 100.0..155/1100
Pratul P. Srinivasan, Boyang Deng, Xiuming Zhang, Matthew Tancik, Ben Mildenhall, and Jonathan Barron. NeRV: Neural Reflectance and Visibility Fields for Relighting and View Synthesis. pp. 7491-7500. 2021 IEEE/CVF Conference on Computer Vision and Pattern doi: 10.11099/VPR44443..222.. 00741. URL https: /www wwwputer.p.........o...... wttp//////pooceee/cooooeeo/////e http://prcoeednnnsaarrrce/////ee article/cvpr/ 102114/50000ns11yeee11a.
Recognition (CVPR), June 2021. ISBN 978-1-6654-45092..
T. Tg. D. M. Tran, H. W. Jensen, R. Ramamoorthi, and J. R. Frisvad. Neural SSS: Lightweight Object Appearance Representation. Computer Graphics 8659. doi: 10. (all1///ff..111 URL Graphierr Forum, 43(4):e15158, 2024. ISSN 1467- 10. 10.1111/cgf 15158.
wtwp:liirraaaaaarrrlaaeaeaeeeeee cwm/doi/ abss/ epprnn: http://wahhhhbbbaaaayyy.y//////d
Delio Vicini, Vladlen Koltun, and Wenzel Jakob. Learned Shape-Adaptive Subsurface Scattering Model. ACM Transactions Graphics, 38(4): August 2019, ISSN 0730-0301, 1557-7368. doi: 10. /41114//63664333222774 URL https::pp...................t. //dl. acm. 11tp:///..1111111111313 3322974.
Kun Xu, Wei-Lun Sun, Zhao Dong, Dan- Dan-Yong Zhao, Run-Dong Wu, and Shi-Min Hu. Anisotropic Spherical Gaussians. ACM Trans. Graph., 32(6):209:1 1-209:11 November 2013. ISSN 0730- 0301. doi: 10.11457250336322563866 2508363. 2508386.
URL https: /dl. acc. org/ http////1111....0
Yingyan Xu, Gaspard Zoss, Prashanth Chandran, Markus Gross, Derek Bradley, and Paulo Go- tardo. ReNeRF: Relightable Neural Radiance Fields with Nearfield Lighting. In 2023 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 22524-22534, Paris. France, Oc- tober 2023. IEEE. ISBN 1000888-503-07888- https:/
doi: 10.110000CC1100000222000666 URL ieee, org/ dww.menn/////cccccccoc /ot.pe/t/133777/////
/ttpeeeeeeppeeeeeeee https::/ieexpporee
Ling-QU Yan, Weilun Sun, Henrik Wann Jensen, and Ravi Ramamoorthi. A BSSRDF Model for Efficient Rendering of Fur with Global Illumination. ACM Transactions Graphies, 36(6):
1-13, December 2017. ISSN 0730-0301, 1557-7368. doi: 10.1145/3130800.3130000
URL
https: acm. wttp:l/awmmooo.oo//////ooo/o//// 311114/55333100111111 3130802.
13

---

Published as a conference paper at ICLR 2026
Keyang Ye, Qiming Hou, and Kun Zhou. 3D Gaussian Splatting with Deferred Reflection. In Special Interest Group on Computer Graphics and Interactive Techniques Conference Conference Papers 24, pp. 1-10, Denver CO USA, July 2024. ACM. ISBN 9798400705250, doi: (0.1145/3641519 3657456. URL https: / /dl acc. org doi / /00000000000 . 11 45 / 641 51 9 3.574456644
Hong-Xing Yu, Michelle Guo, Alireza Fathi, Yen-Yu Chang, Eric Ryan Chan, Ruohan Gao, Thomas Funkhouser, and Jiajun Wu. Learning Object-Centric Neural Scattering Functions for Free-viewpoint Relighting and Scene Composition. Transactions on Machine Learning Re- search, November 2022. ISSN 2835-8856. URL htt.t:/t/////e http://oppeevvieeeee net. forum?ice Nr
Chong Zeng, Guojun Chen, Yue Dong, Pieter Peers, Hongzhi Wu, and Xin Tong. Relighting Neural Radiance Fields with Shadow and Highlight Hints. In ACM SIGGRAPH 2023 Confer- Proceedings, SIGGRAPH '23, pp. 1-11, New York, NY, USA, July 2023. Association for Computing Machinery. ISBN 979-8-4007-0159--7. doi: 10.1 05,114453588433335544933 URL ht https: //tp//dl... acm. org/ 100001//00.00 wee...11111188830 3591444422.99488888 3591482.
Kai Zhang. Fujun Luan, Qianqian Wang, Kavita Bala, and Noah Snavely. PhySG: Inverse Rendering with Spherical Gaussians for Physics-based Material Editing and Relighting. In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1449-5458, Nashville, TN, USA, June 2021a la. IEEE. ISBN 978-1-6665-4450922. doi: 10.1 10.109//CVP4444372221000000 ieee
URL https:t////mmm StRtttt///eeeexppaee
Xiuming Zhang, Pratul P. Srinivasan, Boyang Deng, Paul Dehevec, William T. Freeman, and Jonathan T. Barron. NeRFactor: Neural Factorization of Shape and Reflectance Under Un- known Illumination. ACM Transactions on Graphics, 40(6):237:1-23777 December 2021b. ISSN 0730-0301. doi: 00.115/3477853334880066 URL https: /dl acm. comm.orrg////0/// 1145/3478513 3480496.
14


Reviews:
[{"review_id": "7ftQX5luOR", "reviewer": "Reviewer_xXB7", "summary": "This paper proposes SSD-GS, a physically based, relightable 3D Gaussian splashing method, aiming to address the shortcomings of existing 3DGS methods in modeling complex lighting and materials. Its main contributions include: 1. a four-component reflection model (reflection into diffuse reflection, specular reflection, shadows, and subsurface scattering, modeling each separately); 2. learnable dipole subsurface scattering module: 3. occlusion-aware shadow modeling: 4. a progressive training strategy: 4. introducing each reflection term in stages to improve training stability and decomposition quality.\nResults show that SSD-GS outperforms existing methods in both quantitative metrics (PSNR, SSIM, LPIPS) and visual quality.", "questions": "1. Several recent efforts like already integrate BRDF and light transport in a more consistent manner.  SSD-GS doesn’t compare to these, so it’s hard to judge its relative novelty.\n2.The ablation study is restricted to a single real scene(Pixiu)？", "limitations": "", "rating": 4, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "Clear modular design with interpretable components.\nStrong quantitative performance and solid benchmarks on OLAT datasets.\nThe progressive training schedule is practical and seems to improve stability and disentanglement.", "weaknesses": "1. While the paper shows that this helps convergence, it also adds significant complexity compared to standard 3D Gaussian Splatting.\nEach phase requires manual freezing/unfreezing of modules, separate learning rates, and fine-tuned iteration boundaries (e.g., “freeze scattering between 13K–20K iterations”).\nThis design could make the method hard to reproduce, slow to train, and fragile in new scenes or datasets.\nThus, the practical benefit of SSD-GS might not justify the extra implementation and tuning cost, especially if the final perceptual gain is modest.\n2. The shadow term 𝑆(𝑥) modulates both diffuse and specular, but not subsurface scattering.\n    This is physically incorrect？SSS should also be affected by light occlusion (it’s subsurface, not emission).\n    The “standard dipole” model (Jensen et al., 2001) assumes planar homogeneous semi-infinite media, not Gaussian ellipsoids？\n3. “Physically-based” is used loosely — the method remains a rasterized approximation, not energy-conserving or transport-correct.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "b64F2eImIQ", "reviewer": "Reviewer_9FAa", "summary": "This paper introduces SSD-GS, a Gaussian Splatting–based inverse rendering framework that models subsurface scattering and shadows. The method integrates a dipole diffusion term into each Gaussian to approximate subsurface transport and estimates shadows by aggregating visibility from the light source to each Gaussian across different directions. Experimental results indicate that SSD-GS more accurately captures lighting–surface interactions and outperforms strong baselines.", "questions": "1. The shadow shown in Figure 2 seems unreasonable. For point light sources, we would usually get a hard shadow, but the result in the figure is not like that. Why not use shadow mapping to obtain a hard shadow directly? Is there a reason for this?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1. The work introduces a novel inverse rendering framework. Compared to previous methods that only model the diffuse and specular components, the proposed method additionally models the scattering and shadows, which enables the precise simulation of the complex light transport.\n2. Comprehensive evaluations show consistent gains over competitive inverse-rendering baselines, achieving state-of-the-art results.", "weaknesses": "1. The proposed method performs well on translucent materials. However, for opaque materials (e.g., Hotdog and Lego), it does not appear to show a clear advantage over prior methods. This suggests that the approach may offer limited improvements in modeling diffuse, specular, and shadow components.\n2. Although the ablation study demonstrates the effectiveness of each component, it was conducted on the “Pixiu” case. The authors should provide comparison results of different variants in more cases to give a more detailed and accurate assessment.\n3. The proposed method does not explicitly model indirect illumination. For shadowed regions, light reflected from other surfaces is crucial to accurately simulating light transport.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "V4I6Aahr1C", "reviewer": "Reviewer_8152", "summary": "This paper presents a novel inverse rendering framework, SSDGS, which better modeling the scattering and shadows. The proposed method assigns each Gaussian primitive with additional dipole diffusion term to explicitly model the scattering and integrates the visibility from the light to the projected screen space Gaussian to formulate the shadows. Experiment results are provided to demonstrate the effectiveness of the framework.", "questions": "I have some questions about how shadow terms are calculated. Is the binary visibility obtained through shadow mapping? If so, how does this differ from the weighted sum of accumulated opacities in GS^3? Could you provide some visualization results？", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1. The paper propose a novel inverse rendering framework, where the decomposed reflectance components can faithfully model the complex lighting-material interaction. In particular, the introduction of a subsurface scattering term enables the method to be applied to translucent materials.\n2. The proposed progressive training scheme stabilize the training.\n3. Extensive experiments show that the method achieve best performance over inverse rendering baselines.", "weaknesses": "1. The proposed method does not consider the indirect illumination, which is important to achieve complete decoupling of lighting and materials. It is ture that introducing indirect lighting in OLAT setting is difficult, but there are also some ways to approximate it. For example, GS^3 models the indirect lighting as residual term using an MLP. And since we can do shadow mapping, could we combine the RSM(Reflective Shadow Maps) or its variants into the pipeline to achieve indirect lighting modeling?\n2. SSDGS do gets good results on translucent object by modeling the scattering. But for opaque objects like cat in NRHints datasets, lego and hotdog in GS^3 dataset, SSDGS does not show significant performance gain", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "sNXuFc7XI3", "reviewer": "Reviewer_uu3x", "summary": "The paper proposes SSD-GS, a physically based relighting framework built on 3D Gaussian Splatting (3DGS). It decomposes reflectance into four components, diffuse, specular, shadow, and subsurface scattering, and introduces a learnable, dipole-based scattering module for subsurface transport, along with an occlusion-aware shadow formulation. Experiments show improved relighting quality over prior methods.", "questions": "See weakness.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Introduces multiple components for realistic rendering, including diffuse, specular, shadow, and subsurface scattering.\n\n2. Ablation studies demonstrate the effectiveness of individual components (diffuse, specular, subsurface scattering).\n\n3. SSD-GS achieves strong performance compared with prior work.", "weaknesses": "1. The physical meaning of the soft-shadow term is unclear. In §4.3, it does not appear to integrate occlusion over the Gaussian’s hemispherical visibility; instead, a density-aware per-ray weight is used. This raises concerns about whether the term truly models soft shadowing or merely reweights radiance. Clarification (and, ideally, a derivation or comparison to a visibility integral) would help.\n2. Several common relighting benchmarks, Synthetic4Relight, TensoIR, NeRO, are missing. Evaluating on these datasets would make the results more convincing.\n3. Comparing against 3DGS (which lacks relighting capability) may be unfair. The paper should include more relighting-capable baselines (such as TensoIR, NeRO, R3DG, IRGS, etc.) to strengthen the comparison.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)