Radiometrically Consistent Gaussian Surfels for Inverse Rendering
Authors: Kyu Beom Han
Jaeyoon Kim
Woo Jae Kim
Jinhwan Seo
Sung-eui Yoon
Venue: ICLR 2026 Oral (2026)
Keywords: Radiometric Consistency
Indirect Illumination
Gaussian Splatting
Inverse Rendering
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: Radiometric Consistency for Gaussian Surfels provide accurate indirect illumination for inverse rendering

Abstract: Inverse rendering with Gaussian Splatting has advanced rapidly, but accurately disentangling material properties from complex global illumination effects, particularly indirect illumination, remains a major challenge. Existing methods often query indirect radiance from Gaussian primitives pre-trained for novel-view synthesis. However, these pre-trained Gaussian primitives are supervised only towards limited training viewpoints, thus lack supervision for modeling indirect radiances from unobserved views. To address this issue, we introduce radiometric consistency, a novel physically-based constraint that provides supervision towards unobserved views by minimizing the residual between each Gaussian primitive’s learned radiance and its physically-based rendered counterpart. Minimizing the residual for unobserved views establishes a self-correcting feedback loop that provides supervision from both physically-based rendering and novel-view synthesis, enabling accurate modeling of inter-reflection.
We then propose Radiometrically Consistent Gaussian Surfels (RadioGS), an inverse rendering framework built upon our principle by efficiently integrating radiometric consistency by utilizing  Gaussian surfels and 2D Gaussian ray tracing. We further propose a finetuning-based relighting strategy that adapts Gaussian surfel radiances to new illuminations within minutes, achieving low rendering cost ($<$10ms). Extensive experiments on existing inverse rendering benchmarks show that RadioGS outperforms existing Gaussian-based methods in inverse rendering, while retaining the computational efficiency.

Published as a conference paper at ICLR 2026
RADIOMETRICALLY CONSISTENT GAUSSIAN SURFELS FOR INVERSE RENDERING
Kyu Beom Han, Jaeyoon Kim, Woo Jae Kim, Jinhwan Seo & Sung-Eui Yoon' School of Computing
Korea Advanced Institute of Science and Technology Daejeon, South Korea
{gbhan, kimjy2630 wkim97, jinhwan. seof ac. sungeui @kaissi stsiss.ee edu edu
ABSTRACT
Inverse rendering with Gaussian Splatting has advanced rapidly, but accurately disentangling material properties from complex global illumination effects, par- ticularly indirect illumination, remains a major challenge. Existing methods of- ten query indirect radiance from Gaussian primitives pre-trained for novel-view synthesis. However, these pre-trained Gaussian primitives are supervised only towards limited training viewpoints, thus lack supervision for modeling indirect radiances from unobserved views. To address this issue, we introduce radiometric consistency loss, a novel physically- -based constraint that provides supervision to- wards unobserved views by minimizing the residual between each Gaussian prim- itive's Iearned radiance and its physically-basee rendered counterpart. Minimiz- ing the residual for unobserved views establishes self-correcting feedback loop that provides supervision from both physically-basee rendering and novel-view synthesis, enabling accurate modeling of inter-reflection. We then propose Radio- metrically Consistent Gaussian Surfels (RadioGS), an inverse rendering frame- work built upon our principle by efficiently integrating radiometric consistency by utilizing Gaussian surfels and 2D Gaussian ray tracing. We further propose a finetuning- based relighting strategy that adapts Gaussian surfel radiances new illuminations within minutes, achieving low rendering cost (<10ms). Extensive experiments on existing inverse rendering benchmarks show that RadioGS outper- forms existing Gaussian-based methods in inverse rendering, while retaining the computational efficiency.
Project Page:
io/ www.ooo//ooossss.oooooooooooosss ooaeiiggspppee page//
INTRODUCTION
Inverse rendering, long-standing task in computer vision and graphics, seeks to recover scene properties such as geometry, material, and illumination from one or more input images. Despite its significance, this problem remains non-trivial due to the complex interactions between light and materials, as well as the uncertainty of lighting conditions. Inspired by the remarkable success of neural radiance fields in novel view synthesis (NVS) (Mildenhall et al. (021), recent inverse rendering techniques have adopted these implicit neural representations (Zhang et al., 2021b Boss al. 202T Zhang al., [022aa] Liang et al. 2023). More recently, Gaussian Splatting (Kerbl al. has emerged as a powerful alternative to overcome the computational demands of implicit neural representations.
2023
While Gaussian Splatting allows faster optimization and real-time rendering, modeling complex global illumination effects, particularly indirect illumination and inter-reflections between surfaces, remains significant challenge. Existing Gaussian-based inverse rendering approaches often ad- dress indirect illumination as learnable residual light (Gao et al.) 20244 Liu et al. 2024 et al.) 2024). or obtain incident radiances from NVS-trained Gaussian primitives (Liang et al. 2024|] Shi
et al. 2023: Sun et al. 2025 Gu et al. 2024). These approaches, however, lack supervision for
* *Corresponding Author

---

Published as a conference paper at ICLR 2026
Indirect Radiance
PBIL render
Albedo
Roughness
Normal
IRGS
SVG-IR
Ours
Realistic and detailed indirect illumination modeling
(b) Rendering and decomposition results
budlect
Sxtavet
Sxlick
(c) Relighting results roal-world objoct datasst
Figure 1: We introduce RadioGS, novel inverse rendering framework that models accurate indirect illumination by providing a novel physically-based supervision on unobserved directions. (a) Com- pared to existing maassann-based methods (Gu et al., 2024 Sun al., 2025), method provides realistic inter-reflection between the red bulb and the blobs on the yellow lego surface, (b) leading to robust decomposition of scene properties. (c) Our method can also generate realistic indirect illumination on new lighting conditions for real objects from Stanford-ORB dataset (Kuang et al.
2023
indirect radiances from unobserved directions due to the limited viewpoints available during NVS training. Inaccurate indirect radiances from unobserved directions may lead to incorrect surface and illumination decomposition, such baking indirect lighting effects into the surface.
In this work, we introduce a novel physically-basee based constraint for Gaussian -asaudd----eddd-- inverse rendering, termed radiometric consistency loss, inspired by principles from self-training neural radiance caches for global illumination (Hadadan et al. 202T Müller et al.) 202T). Radiometric consistency aims to reduce the residual between the learned radiances Gaussian primitives and the physically- rendered (PBR) radiances for unobserved directions, generating a self-correcting guidance between view-constrained Gaussian radiances and physical principle induced from the PBR radiances. Our physically-basee constraint allows Gaussian primitives to self-correct their radiances to match the consistency and provide accurate indirect illumination for unobserved viewpoints.
We further propose RadioGS, an inverse rendering framework with efficiently integrated radiomet- ric consistency by employing Gaussian surfels and differentiable Gaussian ray tracing. Furthermore, we introduce an efficient relighting strategy that leverages radiometric consistency to rapidly adapt Gaussian surfel radiances under novel lighting conditions, enabling per-frame rendering time below 10ms by directly utilizing adapted surfel radiances. Thorough experiments multiple inverse ren- dering benchmarks demonstrate that RadioGS shows enhanced disentanglement of inter-reflections from material and geometry reconstruction, leading to superior relighting performance compared to existing Gaussian-based inverse rendering methods both quantitatively and qualitatively. sum- mary, main contributions follows:
Radiometric consistency, a novel physically- constraint that guides Gaussian surfels to self-correet their radiance by enforcing consistency between surfel radiance and physi- cally rendered radiance for unobserved viewpoints.
RadioGS, a novel inverse rendering framework that efficiently integrates radiometric con- sistency based 2D Gaussian ray tracing to accurately model indirect illumination for enhanced inverse rendering performance.
An efficient relighting method that adapts Gaussian surfel radiances under new lighting conditions within a few minutes, achieving notable reduction in rendering time (<10ms).
2

---

Published as a conference paper at ICLR 2026
2
RELATED WORKS
Inverse Rendering with Neural Radiance Fields. Inverse rendering aims to recover and decom- pose scene properties such as geometry, material, and lighting conditions from images (Marschner 1998). Inspired by the success of neural radiance fields (NeRFs) (Mildenhall et al. 2021) for novel view synthesis (NVS), several works leverage NeRF-like neural representations to optimize scene properties (Zhang et al. 2021a b. Srinivasan et al. 2021) guided by the rendering equation [Kajiya 1986). Another line of research focuses modeling indirect illumination to achieve improved disentanglement of lighting conditions using NeRF' (Zhang et al. 2022
Yao et al. 2022
Zhang et al. [0233 Li al. 2024) or directly queries indirect radiance from NVS pre-trained radi-
ance fields (Jin et al. (202)). ). Subsequent works deploy path tracing (Wu et al. 2023 Dai et al. 2024)
or devise enhanced sampling strategies (Attal et al. 2024) to query incident radiances for physically-
based rendering with NeRFs. However, modeling indirect illumination with NeRFs is computation-
ally intensive due to its reliance on volumetric ray marching and numerous neural network queries per ray. Our method provides an efficient representation for modeling indirect illumination with physically- constraints for inverse rendering.
Inverse Rendering with Gaussian Splatting, Recent advances leverage Gaussian primitives (Kerbl et al. 2023) to encode geometry and material information, enabling fast optimization for inverse rendering (Liu al. 2024 Bi al. 2024). However, modeling indirect illumination with Gaus- sian primitives remains a key challenge. Existing methods model indirect radiances as per-Gaussian learnable parameters (Gao al., 2224 Bi et al. 2024 Ye al. 2025), but the unconstrained opti- mization may lead to ambiguous decomposition of illumination and material information. Another Gaussian primitives by baking irradi- ance volumes (Liang et al.) 2024) or using point-based ray tracing (Sun et al. 2025). Yet, NVS- pretrained Gaussian primitives are supervised only towards observed directions, lacking supervision along arbitrary directions for indirect radiances. Recent work (Gu et al. 2024) leverages differ- entiable Gaussian ray tracing to optimize indirect radiances, but the training signal is still derived from synthesizing images of observed views. In contrast to these approaches, our work introduces physically- supervision for unobserved viewpoints by enforcing all Gaussian radiances to sat- isfy the principle of the rendering equation.
line of work queries indirect radiances from NVS-
Self-training Radiance Caches for Global Illumination. Efficiently evaluating the rendering equation (Kajiya, 1986) is central to both rendering and inverse rendering. Classical radiosity (Goral cet al. 1984 [mmel et al. 1986) solves a simplified, diffuse form of rendering equation via linear systems, while radiance caching (Krivánek al., 2005 Krivanek & Gautron 2022) amortizes the computational cost by storing and interpolating light samples. Recent work shows that neural caches can be self-trained to satisfy the rendering equation by iteratively minimizing the renderingeeuaation residual (Müller et al.] 2021 Hadadan et al. 2021), and that such caches provide effective supervi- sion on global illumination for differentiable rendering (Hadadan et al. 2023). We therefore pro- pose an inverse rendering framework that extends these principles to Gaussian primitives, efficiently guiding Gaussian primitives to represent global illumination.
PRELIMINARIES
Gaussian Surfels, also termed 2D Gaussian Splatting (2DGS) (Huang et al. 2024), represent scene with disk-like 2D Gaussian primitives, which are derived form of 3D Gaussian primitives. A Gaussian surfel is expressed using a transformation matrix H E R4x4 that transforms the surfel's local UV space to world space as below:
H Sutu
t,ty
p 0 1
0
0
where tu. tv, (sa, sc), and p refer the two principal tangential vectors, the scaling vector, and the center position, respectively.
Ray-splat intersection is employed to determine the contribution of surfels for final rendering. A Gaussian surfel contains an opacity a and a view-dependent radiance attribute c parameterized by learnalee spherical harmonies coefficients SH,. Each pixel is rendered by alpha-blending of N
3

---

Published as a conference paper at ICLR 2026
depth-sorted Gaussian surfels:
N C = T- T = II (1 on), of = (H,(w)))
3-1
(2)
where C is the final pixel color, Tj the accumulated transmittance, and SH; is the spherical har- monics coefficients parameterization of cj. We utilize the Gaussian surfels the bascline for our inverse rendering framework for robust geometry recovery, and its integration with Gaussian ray tracing (described in S
Physically-basee Rendering (PBR) models the interaction between light and surfaces in scene via the rendering equation (Kajiya 1986). The outgoing radiance L(x, wo) at surface point x in direction w, is defined as follows:
[(x,wo) / fr(2, wa: L2(2, wi)(w: nz )dss
where fr is the bidirectional reflectance distribution function (BRDF), ne is the normal at point x, and (2, w2) is the incoming radiance the point in direction wi.
We assume the target materials for inverse rendering are mostly dielectric, where the diffuse and specular reflectance, fa and (.. of surface point are governed by diffuse albedo a(x) and rough- ness r(tt,, respectively. These parameters define the total reflectance fr based on simplified Disney
BRDF Burley Studios 2012) model the reflectance as below:
a(x)
DFG
(x,,Woo, wi) = fa(x) + fa fa wo)
(4)
π
4(1g (1) Wo)
where D, F, and G are the normal distribution function, the Fresnel term, and the geometry term, respectively, which depend roughness r(v).
Incoming radiance 142 may result directly from the light source or through indirect bounces of other surfaces, depending on the visibility the surface. Thus, we model the incoming radiance below:
(5) where is the visibility at the surface point x with respect to the direction Wi, and Lair and Lind the corresponding direct and indirect incident radiance terms. We note that Lair is independent of x when light sources are distant.
Lx(x,,) = V V(x, w:) Lai (I,w:) + Lind(x wi),
METHOD
In this section, we first introduce our novel physically-bar based regularization termed radiometric con- sistency. Building on this, present inverse rendering framework called Radiometrically Con- sistent Gaussian Surfels (RadioGS), followed by our efficient relighting method based on our radio- metric consistency.
4.1 RADIOMETRIC CONSISTENCY FOR GAUSSIAN SURFELS
Modeling accurate indirect illumination and inter-reflections between Gaussian surfels is crucial for robust decomposition of lighting and material information. Recent GS-based inverse rendering methods query indirect radiance directly from Gaussian surfels, but the surfel radiances are super- vised only through reconstruction from the training images. As a result, surfel radiances along directions that are unseen by camera rays can lead to arbitrary values while still fitting the training images, degrading the stability and accuracy of indirect illumination (top-right diagram of Fig. [ To address this issue, we introduce radiometric consistency, novel physically-base constraint that guides Gaussian surfel radiances for unobserved directions based on the physically-basee rendering process (bottom-right diagram of Fig. 2
4.1.1 FORMULATION
We consider a set of Gaussian surfels G = {9 pretrained for novel-view synthesis (NVS) with each surfel Gj. Let denote the surfel radiance position towards direction war Lo (2r, wa),
4

---

Published as a conference paper at ICLR 2026
Illustaatonn of Proposing Radio
Indirect Illumination Supervision
Radiometric Consistency
2D Gaussion Ray Tracing
Li
Surfl milianee
PBR muliance
Learnable Emv. Map
Goussian Surfala
Physically-hueed Remdering
NVS pre traized GS
Protniion Anrilate fe Moniaa
Rasterince
(un)
Forward
Barloward
RadioGS
Figure 2: Overview of our RadioGS. Left: Our radiometric consistency loss Frad provides physically- supervision on indirect radiances from views unobserved by image reconstruction loss Crecon, by enforcing consistency between surfel radiance LG and physically-basee rendered (PBR) radiance LPBR Gaussian surfels. Radiometric consistency seamlessly integrated into the inverse rendering framework, guiding Gaussian surfels to obtain physically-basee radiance for delivering realistic indirect radiance to other surfels. 2D Gaussian ray tracing is deployed to jointly optimize ray-traced Gaussian surfels with our radiometric consistency loss. Right: Black-dotted ar- rows show NVS supervision, which leaves the occluded green Gaussian unconstrained. -otted arrows show radiometric consistency providing additional supervision its outgoing radiance along unseen directions (e.g., towards other Gaussian surfels).
which is queried from spherical harmonics coefficients of the corresponding Gaussian surfel. Each surfel has optimizable parameters for albedo and roughness. Direct illumination Lasr (wsr is repre- sented by an environment cubemap for inverse rendering.
The core principle of our radiometric consistency is that the learned outgoing radiance of surfel should match its mhysically-rendeee radiance, as dictated by the rendering equation (Eq 3 We formulate our principle as a residual minimization problem. Following Eq, 3 the residual can be expressed as the difference between the surfel radiance LG and physically-baser rendered radiance I.B.R as below:
PBR (r) = 1 (x, wor G) (V w;; Giddir((W)) + Lind(ar, wi; G)) (wi )add (6) LG
Ro(x, w.) La (zz, w.) L
(x, wo),
where L PBR is the radiance calculated by physically- rendering, f+(:G), (-: G), and Lind( ; G) are the BRDF, visibility, and indirect light induced by Gaussian surfels G based on Eq. and Eq. respectively.
Our radiometric consistency aims reduce the 1 -n-ommm of the residual over all Gaussian surfels and all possible directions wo denoted as Lrad.
Crad Bj., RGlll..
(8)
Minimizing the residual norm ||Ra|h establishes self-correcting feedback loop based the rendering equation. On one hand, the physically-rendered radiance T.PBR serves as a physically grounded target, guiding the surfel radiance T-G to represent global illumination for unobserved viewpoints, based on the rendering equation. On the other hand, the well-constrained surfel radi- ances LG towards camera viewpoints provide strong supervisory signal that is propagated the surfel radiances contributing to the indirect illumination term Lind of Eq. This synergistic process allows the Gaussian surfels to obtain physically grounded radiances, thereby providing physically- induced illumination for other surfels.
4.1.2 2D GAUSSIAN RAY TRACING AND MONTE CARLO SAMPLING
Obtaining the visibility (-: G) and indirect radiance Lina(:: G) from Gaussian surfels is critical for creating our self-correcting feedback loop based the inter-reflection among surfels. Point-based

---

Published as a conference paper at ICLR 2026
ray tracing has been applied to precompute visibility (Gao et 2024 Guo et al. 2024) and to query indirect radiance (Sun et al. (025) from Gaussian primitives, but lacks the differentiability and speed required for use during optimization. Inspired by recent works leveraging differentiable Gaussian et al. 2024, Xie et al.] 2024), we deploy a 2D Gaussian ray tracer from IRGS (Gu et al. 2024) leverage optimization through ray-traced surfels for radiometric consistency. 2D Gaussian ray tracing brings seamless integration with our Gaussian surfels by sharing the same ray-splat intersection that defines the contribution of Gaussian surfels.
ray tracing (Moenne-Loccome
Given a ray with the origin x and the direction wi, our ray tracer Trace(z, wi; G) - (Ltraoe: T gathers Gaussian surfels intersecting the ray and returns accumulated radiance Lirace and the final transmittance T Troce following the alpha-blending process of Eq. 2 We use ray-traced radiance Tirace
Ttraae directly as indirect radiance (a, w;; G) and the complement of transmittance
as visibility V(r, w;; G)). respectively. Using our ray tracer, we acquire the Monte Carlo estimate of the integral in Eq. [ as below:
N. Jr War wis G) (V(r,, (L) + Linalar wi G)) (we n2gh (9)
2x
PBR LG
(x, wo) Rd
G);
N.
i=1
where we uniformly sample N, incident directions wi over the hemisphere defined by the surfel normal 12g.
We also perform Monte Carlo sampling on Gaussian surfels G and direction w, for residual estima- tion. We randomly sample N3 surfels for each optimization step, and also sample random directions on the hemisphere defined by the normal of each sampled surfel generate guidance towards un- observed directions. In addition, we additionally sample the directions towards camera viewpoint to propagate well-constraint supervisory signal to ray-traced Gaussian surfels. In conclusion, our design for residual estimation allows us to efficiently deploy radiometric consistency, generating self-correcting training signals explicitly for surfel radiance LG and PBR radiance L PBR to satisfy the physical constraint of the rendering equation.
4.2 INVERSE RENDERING WITH RADIOMETRICALLI CONSISTENT GAUSSIAN SURFELS
In this section, we introduce our inverse rendering framework RadioGS, optimizing Gaussian surfels for inverse rendering under the physically-base constraints from our radiometric consistency. Our framework operates in two stages to ensure both stable training and accuracy. We then introduce our efficient relighting strategy based on radiometric consistency.
Initialization. Existing works initialize geometry via NVS pre-training prior to tackling inverse ren- dering. To incorporate our physically-based constraint during initialization, we additionally intro- duce a simplified version of our radiometric consistency loss, using an efficient split-sum approxima- tion (Munkberg al. 2022) instead of the Monte Carlo estimate. Our approximation avoids training instability from oscillating geometry during the early optimization stage, resulting in robust ge- ometric foundation that is efficiently regularized based on our physically-basee
constraint (see the table of Figure for ablation). Following 2DGS (Huang et al. 2024), we apply image reconstruction loss Crecon to images rasterized by surfel radiance LG. depth distortion loss Ldists normal-depth consistency loss L.. normal smoothing loss Lns. and mask-entropy loss Lmask. We also add image reconstruction loss CPBR to images rasterized by ressically-rendere radiance LB which is approximated using the split-sum approximation. Thus, the total loss for the initialization stage is weighted sum of the loss components as below:
Linit Lrecom
PBR recon
+ XradLrad + Adist Edist + Auhn + AnoLus + Am Lym-
(10)
Inverse Rendering. With our initialized Gaussian surfels, we proceed the main inverse rendering stage by leveraging the full Monte Carlo-estimated radiometric consistency loss Crad to accurately model complex inter-reflections. We additionally use smoothing losses for rasterized albedo and roughness, denoted as Las and Cr., to encourage spatial coherence of material features. Finally, a light prior loss Llight (Liu et al., (203)) is applied to encourage the rendered incident diffuse illumination to adopt natural white appearance. Thus, the total optimization objective for inverse rendering a weighted sum of loss components as below:
Liny Linit dasLas + drshrs Atight Liight-
(11)

---

Published as a conference paper at ICLR 2026
Table Quantitative comparisons on TensoIR dataset (Jin et al. 2023). The results are colored in rank as 1st, 2nd, and 3rd. Our method surpasses existing Gaussian based methods and a NeRF-basee method in most metries, while maintaining the computational efficiency with the average training time of hour. We report our relighting metric using Gaussian ray tracing (Ours) and finetuning- based method (Ours*).
Novel View Synthesis
Normal MAE
Albedo
Relight
Training hours
Method
PSNR
SSIM
LPIPS
PSNR
SSIM
LPIPS
PSNR f
SSIM f
LPIPS
Tensolk
35.09
0.976
0.040
4.100
29.27
0.950
0.085
28.58
0.944
0.081
GS IR
35.33
0,974
0.039
4,948
29.94
0.921
0.100
24.37
0.885
0.096
0.5
GI-GS
36.75
0.972
0.037
5,253
29.90
0.921
0.099
24.70
0.886
0.106
0.5
RSDG
33.35
0,964
0.041
5,927
29.27
0
0.078
27.37
0.909
0.083
1.1
IRGS
35.43
0.964
0.049
4.209
30.62
0.956
0.072
29.91
0.935
0.076
0.9
SVG-IR
36.71
0.033
30.48
31.10
0.946
0.056
Our
Ours
37.86
0,980
0.027
3.689
31.05
0.952
0.072
32,09
31.41
0.953
0.948
0.048
0.052
Please refer to A for the details of additional loss functions Frecons CPBR Llight, and the learning rates of Gaussian surfel parameters.
fuss f.ms faer Ly and
Relighting. Once the lighting condition changes, surfel radiances cannot provide indirect illumina- tion, since they are specifically optimized for the previous lighting condition. Instead, we query indi- rect radiances following IRGS (Gu et al. 2024) by alpha- blending the normal, albedo, and roughness towards the incident direction using Gaussian ray tracing, and applying split-sum approximation to efficiently estimate the incident radiance. However, storing numerous incident radiances per surfel and re-estimating outgoing radiances based on Eq. consumes additional rendering time.
To this end, we introduce a finetuning- based relighting approach that leverages radiometric consis- tency. Radiometric consistency allows surfel radiances rapidly adapt to new lighting conditions. Given a new lighting condition, we perform a few finetuning iterations exclusively on the surfel radiances by minimizing our radiometric consistency loss Lyad- Once finetuning is complete, the scene can be rendered from any viewpoint using only surfel radiances. Please refer to Figure 19 of Appendix additional illustration on our finetuning method.
5 EXPERIMENTS
5.1 EXPERIMENTAL SETUPS
Dataset and Metric. We evaluate our method's novel view synthesis (NVS), inverse render- ing, and relighting capabilities using two synthetic datasets: TensoIR (Jin et al. 2023) and Syn- thetic4Relliht (Zhang et al. 2022). These two synthetic datasets provide diverse lighting conditions and ground truth for geometry and material evaluation. We employ PSNR, SSIM, and LPIPS for evaluating NVS, albedo, and relighting. Normal reconstruction is evaluated using Mean Angular Er- (MAE), and roughness is evaluated using Mean Square Error (MSE). We also provide qualitative relighting results on a real-world object dataset Stanford-ORB (Kuang et al. 2023) in Figure (c)..
Implementation Details. For our radiometric consistency loss Lrad, we set the weight Xyad 0.2. We sample N, = 4096 Gaussian surfels and N, = 64 incident rays per surfel, resulting in 218 rays traced through Gaussian surfels to calculate the radiometric consistency loss at every training iteration. We store the ray-traced results on sampled Gaussians at each step for use in physically- rendered image. For our relighting method, we set the weight Xyad 1.0, and discard all other losses. Experiments were conducted on an NVIDIA RTX 4090 GPU, with total optimization taking approximately 60 minutes (30 for initialization and 30 for inverse rendering), and the finetuning process taking approximately 2 minutes. Please refer to Appendix A.4 for further details.
Baselines. We compare our method against prior Gaussian Splatting (GS)-based methods: GS- IR (Liang et al. (2044, GI-GS (Chen et al. 2024). R3DG (Gao et al. (2024), IRGS (Gu et al.) 22024) and SVG-IR (Sun et al. 2025). We also include TensoIR (Jin et al. 2023). , an efficient NeRF-based approach. Quantitative and qualitative results are reproduced using the publicly available code.
5.2 INVERSE RENDERING PERFORMANCE COMPARISONS
7

---

Published as a conference paper at ICLR 2026
TensoIR
TRGS
SVG-IR
NVS
Albedo
Normal
Relight
(freeplcce)
Relight
(loees))
Figure 3: Qualitative result on the "lego" scene of TensolR dataset. Our method provides en- hanced decomposition and realistic relighting results compared Gaussian-based methods. Specif- ically, our method shows noticeably robust performance regions with high geometric complexity, such as the highlighted bucket. Best viewed in zoom.
TensoIR. On the TensoIR dataset (Table D. our approach demonstrates superior performance various metrics in- cluding novel-view synthesis (NVS), normal estimation, and relighting compared existing methods. Notably, our method outperforms other ray may-tracing based meth- ods Gaussian primitives (Guu et al., 2024 [Sun al.) 2025), reflecting the necessity of physically- hased con- straints surfel radiances in inverse rendering. More- over, our finetuning-base based relighting method outperforms existing relighting methods, indicating the effectiveness of our self-correcting guidance from radiometric consis-
TensoIR PSNE: 50.30
TRGS PyNit 31.66 Timee drtmm
SVG-IR PSNE: Time 10me
Threr cowe
tency.
Qualitative results on Figure fillustrate our method's per- formance on reconstructing finer geometric details for nor- mal reconstruction and NVS, which leads to more realistic relighting results. Figure (a) showcases the realistic and detailed indirect illumination modeled by our method on the same scene compared to the other competitors.
Ours PSNI:: 30.65 Time Imm.
Ours (Finetined) PHNN 37 Time: 4.15mm
GT (Eor: Ficeplnco)
Figure 4: the "armadillo" scene of TensoIR dataset.
Relighting results on
Additional comparisons on relighting (Figure show that both of our relighting methods achieve realistic relighting results, showing real-time rendering capabilities. Especially, our finetuning bassed method shows the fastest rendering time, with minor compromise in quality compared to ray- tracing based relighting. This because finetuning process accumulates minor errors from estimated geometry and material properties of Gaussian surfels into surfel radiances, leading to the trade-off additional comparison of our relighting method.
in visual quality. Figure 20 of
Senthetic4Relight. Results the Syethetic4Relight dataset (Table 2 further validate the capabilities of our method, outperforming existing methods in NVS, albedo reconstruction and relighting, while showing comparable performance on roughness estimation. Vi- sual comparisons on ( how our re- alistic modeling of indirect illumination leads to en- hanced albedo reconstruction and NVS. The inter- reflecting directions of the highlighted region are overlooked during novel-view synthesis training, whereas radiometric consistency provides physically- based constraint on surfel radiances towards reflecting directions, resulting in realistic indirect illumination. Please refer to Appendix, D for additional qualitative results.
Table 2: Quantitative comparisons Synthetic4Relight dataset.
NVS
Roughness
Albedo
Relight
Mathod
PSNR
MSE 1
PSNR
PSNR
R3DG
34.10
0.010
28.65
33,12
IRGS
34.44
0.008
30.50
34.35
SVG-IR
34.14
0.009
29.06
32,59
Ours
34.98
0.011
30.69
34.87
8

---

Published as a conference paper at ICLR 2026
IRGS
SVG-IR
Ours
GT
Indiroct
Albodo PSNT-29.49
Indireet Rodlance
Albedo
Indirect Radiance
PSNT: 28.25
Radiancc
PSNR-25- 28.49
Rediance
Religit P8NR:31127
Relight PSNR:1L.10
NVS PSNR:34.14
Relight PSNR:32228
NVS
Relight.
PSNIt:33.62
PSN11:3392
Figure 5: Qualitative results the "hotdog" scene of Synthetic4Relight (Zhang al., 2022 dataset. Our method models natural inter-reflection between the sausages and the buns, showing superior reconstruction performance highlighted regions. IRGS shows relatively bright and fluc- tuating indirect illumination, which led to darker albedo reconstruction. SVG-IR models relatively darker indirect illumination, returning brighter albedo reconstruction. Best viewed in zoom.
Indirect Ellumination
Albedo
Albedo Ll
wy Fyad
NVS
Albedo
Relight
Method
PSNR
PSNR
PSNR
Ours
37.86
31.05
32.09
Arad 0
35.82
30.82
31.69
Detach EPER
37.25
30.67
32.05
Detuch L.c
37.46
28.68
31.42
NVS init.
37.43
30.34
31.66
Figure 6: Ablation studies our radiometric consistency. The left sub-figure demonstrates how our radiometric consistency loss Crad provides guidance on radiances towards unobserved views such as the interstices, leading to enhanced albedo reconstruction (red box). Also, our method guides the generation of inter-reflections between the ketchup and the plate (yellow box). The right table contains PSNR metrics for the ablation studies.
5.3 ABLATION STUDIES RADIOMETRIC CONSISTENCY
We report ablation studies components of radiometric consistency the TensolR dataset. The table Figure shows the PSNR metrics for three categories, NVS, albedo reconstruction, and relighting, in our ablation studies.
Absence of Radiometric Consistency. We perform ablation studies on the radiometric consistency by removing the radiometric consistency loss during the inverse rendering stage (see the left sub- figure of Figure 6 and "Arad 0" on the right table of Figure (6). The absence of radiometric consistency provides incorrect indirect radiances on unobserved directions, degrading the albedo reconstruction on the corresponding regions and leading to significant performance degradation in all three categories. Further analysis on indirect illumination modeling and inverse rendering are provided in Appendix(E]
Detaching Gradient Flows from Crad- We ablate on the self-correcting gradient flow by detach- ing the gradients towards Gaussian surfels during the calculation of the surfel radiance Lo and physically- rendered radiance T.PRR on Eq. Detaching either gradient leads to an overall performance drop. Detaching gradient from LG cause noticeable degradation albedo reconstruc- tion, while detaching gradient from L degrades NVS. Such degradation reflects the importance of the view-constrained supervision signal from LG. and the physically- based constraint delivered by LPBR
Initialization. We found that removing radiometric consistency during initialization degrades overall performance. highlighting the contribution our radiometric consistency as beneficial physically-base guidance for initialization.

---

Published as a conference paper at ICLR 2026
6 CONCLUSION AND FUTURE WORKS
We introduced novel physically-basee based supervision called radiometric consistency, which addresses the key challenge of modeling indirect illumination in Gaussian- based representations by guiding Gaussian surfels to learn accurate indirect illumination towards unobserved directions. We then in- troduced Radiometrically Consistent Gaussian Surfels (RadioGS), a novel inverse rendering frame- work that efficiently leverages radiometric consistency by utilizing 2D Gaussian ray tracing. We also presented a new relighting method that leverages our constraint to quickly adapt surfel radiances to new lighting environments, achieving a rendering time below 10ms per frame. Experiments demon- strated that RadioGS outperforms existing Gaussian-based methods on two synthetic benchmarks, based on accurate and realistic indirect illumination. Since the current method only supports dielec- tric materials, extending radiometric consistency to more complex materials, such as anisotropic or highly-reflective surfaces, would be an interesting future direction.
ACKNOWLEDGEMENTS
This work supported by the Institute of Information communications Technology Planning Eval- uation (ITP)) grant (No. tency Approach to Embodied AGI through Constructing and Reasoning in the Real World; LG AI STAR Talent Development Program for Leading Large-Scale Generative AI Models in the Physi- cal AI Domain (No. RS-2025-25442144 and IITP(Institute of Information Coummunications Technology Planning & (iaaatioonn-TRRCCIooooaiio by the Korea govemmenn(Ministty of Science and (ITT RS-2020-112014660)
Physically -ogounddd Intelligence: A Dual Compe-
Technology Research Center) grant funded
REFERENCES
Benjamin Attal, Dor Verbin, Ben Mildenhall, Peter Hedman, Jonathan T Barron, Matthew O' O'Toole, and Pratul P Srinivasan. Flash cache: Reducing bias in radiance cache based inverse rendering. In European Conference Computer Vision, pp. 20-36. Springer, 2024.
Jonathan T Barron, Ben Mildenhall, Dor Verbin, Pratul P Srinivasan, and Peter Hedman. Mip-nerf 360: Unbounded anti-aliased neural radiance fields. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 5470-5479, 2022.
Zoubin Bi. Yixin Zeng. Chong Zeng, Fan Pei, Xiang Feng, Kun Zhou, and Hongzhi Wu. Gs3: Efficient relighting with triple gaussian splatting. In SIGGRAPH Asia 2024 Conference Papers, pp. 1-12, 2024.
Mark Boss, Raphael Braun, Varun Jampani, Jonathan T Barron, Ce Liu, and Hendrik Lensch. Nerd: Neural reflectance decomposition from image collections. In Proceedings of the IEEE/CVF In- ternational Conference on Computer Vision, pp. 12684-12694 2021.
Brent Burley and Walt Disney Animation Studios. Physically-based shading at disney. In Acm Siggraph, volume 2012. pp. 1-7. vol. 2012, 2012.
Hongze Chen, Zehong Lin, and Jun Zhang, Gi-gs: Global illumination decomposition on gaussian splatting for inverse rendering. arXiv preprint arXiv:2410.02619, 2024.
Yuxin Dai, Qi Wang, Jingsen Zhu, Dianbing Xi, Yuchi Huo, Chen Qian, and Ying He. Mirres: Multi- bounce inverse rendering using reservoir sampling. arXiv preprint arXiv:2406. 16360, 2024.
Jian Gao, Chun Gu, Youtian Lin, Zhihao Li, Hao Zhu, Xun Cao, Li Zhang, and Yao Yao. Re- lightable 3d gaussians: Realistic point cloud relighting with brdf decomposition and ray tracing. In European Conference on Computer Vision, pp. 73-89. Springer, 2024.
Cindy M Goral, Kenneth E Torrance, Donald P Greenberg, and Bennett Battaile. Modeling the interaction of light between diffuse surfaces. ACM SIGGRAPH computer graphics, 18(3):213- 222, 1984.
10

---

Published as a conference paper at ICLR 2026
Chun Gu, Xiaofei Wei, Zixuan Zeng, Yuxuan Yao, and Li Zhang. Irgs: Inter-reflective gaussian splatting with 2d gaussian ray tracing. arXiv preprint arXiv:2412.15867, 2024.
Yijia Guo, Yuanxi Bai, Liwen Hu, Ziyi Guo, Mianzhi Liu, Yu Cai, Tiejun Huang, and Lei Ma. Prtgs: Precomputed radiance transfer of gaussian splats for real-time high-que nigh-quality relighting. In Proceedings of the 32nd ACM International Conference on Multimedia, pp. 5112-5120, 2024.
Saeed Hadadan, Shuhong Chen, and Matthias Zwicker. Neural radiosity. ACM Transactions on Graphics (TOG), 10(6):1-11, 2021.
Saeed Hadadan, Geng Lin, Jan Novák, Fabrice Rousselle, and Matthias Zwicker. Inverse global illumination using neural radiometric prior. In ACM SIGGRAPH 2023 Conference Proceedings, pp. 1-11, 2023.
Binbin Huang, Zehao Yu, Anpei Chen, Andreas Geiger, and Shenghua Gao. 2d gaussian splatting for geometrically accurate radiance fields. In ACM SIGGRAPH 2024 conference papers, pp. 1-11, 2024.
2024.
David S Immel, Michael F Cohen, and Donald P Greenberg. A radiosity method for non-diffuse environments. Acm Siggraph Computer Graphics, 20(4):133-142, 1986.
Haian Jin, Isabella Liu, Peijia Xu, Xiaoshuai Zhang, Songfang Han, Sai Bi, Xiaowei Zhou, Zex- iang Xu, and Hao Su. Tensoir: Tensorial inverse rendering. Proceedings of the IEEF/CVF Conference Computer Vision and Pattern Recognition, pp. 165-174, 2023.
James T Kajiya. The rendering equation. Proceedings of the 13th annual conference on Computer graphics and interactive techniques, pp. 143-150, 1986.
Bernhard Kerbl, Georgios Kopanas, Thomas Leimkühler, and George Drettakis. 3d gaussian splat- ting for real-time radiance field rendering. ACM Trans. Graph., 12(4):139-1, 2023.
Jaroslav Krivanek and Pascal Gautron. Practical global illumination with irradiance caching. Springer Nature, 2022.
Jaroslav Krivánek, Pascal Gautron, Sumanta Pattanaik, and Kadi Bouatouch. Radiance caching for efficient global illumination computation. IEEE Transactions Visualization and Computer Graphics, 1(5):550-561, 2005.
Zhengfei Kuang, Yunzhi Zhang, Hong-Xing Yu, Samir Agarwala, Elliott Wu, Jiajun Wu, et al. Stanford-orb: a real-world 3d object inverse rendering benchmark. Advances in Neural Informa- fion Processing Systems, 66:46933-469577 2023.
Jia Li, Lu Wang, Lei Zhang, and Beibei Wang. Tensosdf: Roughness-aware tensorial representation for robust geometry and material reconstruction. ACM Transactions on Graphics (TOG), 43(4): 1-13, 2024.
Ruofan Liang, Huiting Chen, Chunlin Li, Fan Chen, Selvakumar Panneer, and Nandita Vijaykumar. Envidr: Implicit differentiable renderer with neural environment lighting. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 79-89, 2023.
Zhihao Liang, Qi Zhang, Ying Feng, Ying Shan, and Kui Jia. Gs-ir: 3d gaussian splatting for inverse rendering. Proceedings of the IEEF/CVF Conference on Computer Vision and Pattern Recognition, pp. 21644-21653 2024.
Yuan Liu, Peng Wang, Cheng Lin, Xiaoxiao Long, Jiepeng Wang. Lingjie Liu, Taku Komura, and Wenping Wang. Nero: Neural geometry and brdf reconstruction of reflective objects from multi- view images. ACM Transactions on Graphics (ToG), 12 2023.
Zhenyuan Liu, Yu Guo, Xinyuan Li, Bernd Bickel, and Ran Zhang. Bigs: Bidirectional gaussian primitives for relightable 3d gaussian splatting. arXiv preprint arXiv:2408. 13370, 2024.
Stephen Robert Marschner. Inverse rendering for computer graphics. Cornell University, 1998.
11

---

Published as a conference paper at ICLR 2026
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. Communications of the ACM, 65(1):99-106, 2021.
Nicolas Moenne-Loccozz Ashkan Mirzaei, Or Perel, Riccardo de Lutio, Janick Martinez Esturo, Gavriel State, Sanja Fidler, Nicholas Sharp, and Zan Gojeic. 3d gaussian ray tracing: Fast tracing of particle scenes. ACM Transactions on Graphics (TOG), 43(6):1-19, 2024.
Thomas Müller, Fabrice Rousselle, Jan Novák, and Alexander Keller. Real-time neural radiance caching for path tracing. arXiv preprint arXiv:2 12372, 2021.
Jacob Munkberg, Jon Hasselgren, Tianchang Shen, Jun Gao, Wenzheng Chen, Alex Evans, Thomas Müller, and Sanja Fidler. Extracting triangular 3d models, materials, and lighting from images. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp- 8280-8290, 2022.
Steven G Parker, James Bigler, Andreas Dietrich, Heiko Friedrich, Jared Hoberock, David Luebke, David McAllister, Morgan McGuire, Keith Morley, Austin Robison, et al. Optix: a general purpose ray tracing engine. Acm transactions on graphics (tog), 29(4):1-13, 2010.
Yahao Shi, Yanmin Wu, Chenming Wu, Xing Liu, Chen Zhao, Haocheng Feng. Jian Zhang, Bin Zhou, Errui Ding, and Jingdong Wang. Gir: 3d gaussian inverse rendering for relightable scene factorization. arXiv preprint arXiv:2312.05133, 2023.
Pratul P Srinivasan, Boyang Deng. Xiuming Zhang, Matthew Tancik, Ben Mildenhall, and Jonathan T Barron. Nerv: Neural reflectance and visibility fields for relighting and view syn- thesis. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 7495-7504 2021.
Hanxiao Sun, Yupeng Gao, Jin Xie, Jian Yang, and Beibei Wang. Svg-ir: Spatially-varying gaussian splatting for inverse rendering. In Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 16143-11100 16152, 2025.
BovikAC WangZhou, Hamid R Sheikh, et al. Image qualityassessment: From emorvisibilltytossuuc tural similarity. IEEE Transon ImageProcessing, 13(4))600, 2004.
Haoqian Wu, Zhipeng Hu, Lincheng Li. Yongqiang Zhang, Changjie Fan, and Xin Yu. Nefii Inverse rendering for reflectance decomposition with near-field indirect illumination. In Proceedings of the IEEF/VVF Conference on Computer Vision and Pattern Recognition, pp. 4295-4304, 2023.
Tao Xie, Xi Chen, Zhen Xu, Yiman Xie, Yudong Jin, Yujun Shen, Sida Peng. Hujun Bao, and Xiaowei Zhou. Envgs: Modeling view-dependent appearance with environment gaussian. arXiv preprint arXiv:2412. 15215, 2024.
Yao Yao, Jingyang Zhang, Jingho Liu, Yihang Qu, Tian Fang, David McKinnon, Yanghai Tsin, and Long Quan. Neilf: Neural incident light field for physically-based material estimation. In European conference on computer vision, pp. 700-716. Springer, 2022.
Kai Ye, Chong Gao, Guanbin Li, Wenzheng Chen, and Baoquan Chen. Geosplatting: Towards geometry guided gaussian splatting for physically- inverse rendering. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp.
2025.
Jingyang Zhang, Yao Yao, Shiwei Li, Jingbo Liu, Tian Fang, David McKinnon, Yanghai Tsin, and Long Quan. Neilf++: Inter-reflectable light fields for geometry and material estimation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3601-3610, 2023.
Kai Zhang, Fujum Luan, Qianqian Wang, Kavita Bala, and Noah Snavely. Physg: Inverse rendering with spherical gaussians for physics-based material editing and relighting. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pp. 5453-5462, 2021a.
Xiuming Zhang, Pratul P Srinivasan, Boyang Deng, Paul Debevec. William T Freeman, and Jonathan T Barron. Nerfactor: Neural factorization of shape and reflectance under an unknown illumination. ACM Transactions on Graphics (ToG), 40(6): 2021b.
12

---

Published as a conference paper at ICLR 2026
Yuanqing Zhang, Jiaming Sun, Xingyi He, Huan Fu, Rongfei Jia, and Xiaowei Zhou. Modeling indi- rect illumination for inverse rendering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2022.
13

---

Published as a conference paper at ICLR 2026
APPENDIX
THE USE OF LLMS
The author(s) used ChatGPT for minor grammatical adjustments, and all resulting edits were care- fully reviewed and finalized by the author(s).
A IMPLEMENTATION DETAILS
In this section, we discuss additional details of the implementation of work.
A.1 DEPTH INTERPOLATION
Depth maps of our Gaussian surfels are rendered by interpolation of the Gaussian primitives:
o.T:
D
(12)
where di the depth of i-th primitive. This formulation ensures the depth map reflects the visibility- weighted contribution of all overlapping primitives.
A.2
LOss FUNCTIONS
A.2.1
RECONSTRUCTION LOSS
The reconstruction loss is composed of weighted sum of lllss and SSIM (WangZhou al.,
2004). Following our baseline Huang et al. (202)), we assign a weight of 0.8 to the I loss and 0.2 SSIM.
A.2.2
DISTORTION LOSS
The depth distortion loss enforces geometric consistency along rays by minimizing the weighted pairwise depth differences between Gaussian primitives:
Ldiet Σ 3.f
(13)
where denotes the depth value of the i-th primitive. The loss drives Gaussian primitives to collapse into tight clusters aligned with surface geometry and enhances depth coherence.
A.2.3 NORMAL-DEPTI CONSISTENCY LOSS
This loss enforces geometric coherence by aligning Gaussian primitive normals with surface geom- etry derived from depth gradients:
Cn Σ - N)
(14)
where n, is the normal vector i-th primitive and N is the surface normal at the median of inter- section Ps estimated from gradient of depth map:
PPP
yPs
(15)
V
X V Vpps|
A.2.4 FIRST-ORDER EDGE AWARE SMOOTHING LOSS
We use edge-aware smoothing constraints to enhance spatial coherence while preserving structural edges for surface normal, albedo, and roughness predictions. These losses minimize the gradient of each feature and relax smoothing constraints at image edges:
((n.,r)s | (V,, A,, RH exp(-IIVC,..I),
(16)
where N. A, and R are rendered normal, albedo, and roughness map, respectively and Cy. is the ground truth training image.
14

---

Published as a conference paper at ICLR 2026
A.2.5 SPARSITY LOSS
The sparsity loss drives Gaussian's opacity towards or
1 ac
C.
[lo((a)) + log(1 - a:)]
(17)
It collapses the spatial distribution of Gaussian primitives into thin surface-aligned layers and accel- crates ray tracing by reducing hits and sorting via early termination.
A.2.6 LIGHT PRIOR LOOSS
The light prior loss enforces neutral white illumination in diffuse rendering. It constrains the per- channel average intensities è of estimated lighting:
Llight Σ 1=1
3
36g
(18)
A.3 2D GAUSSIAN RAY TRACER
We implemented 2D Gaussian ray tracer using Pytorch CUDA extensions and OptiX (Parker al. 2010) following Moenne-Loccoz et al. (2024) and Gu et al. (0024). We adopt simpler BVH construction with two triangles encapsulating the 2D Gaussian primitives from Xie et al. (2024), reducing the BVH update each training iteration from 3ms 2ms. The Gaussian response is achieved by analytically calculating the intersection point between the flat 2D Gaussian primitive with the center y and normal and the ray with origin and direction d as below:
p
n (1s d) o.
p =
(19)
n d
Such formulation is identical to the one that of the 2DGS Huang et al. (2024) rasterizer, ensuring consistent Gaussian response between the rasterizer and the ray tracer.
To reduce the computation of depth-sorting ray-traced Gaussians, we utilize any-hit program to gather Gaussians within the buffer. Once the buffer is full, we sort the gathered Gaussians by depth, and accumulate the radiance and trasmittance based on Eq. 2 The process repeats to gather the next & Gaussians until all ray-traced Gaussians are accumulated or the transmittance reaches the threshold. We use buffer of K=16 for sorting Gaussians per ray, and terminate the tracing when with the transmittance threshold of 0.03. For differentiabilityy we re-cast the rays to gather the same set of Gaussians, and analytically calculate the gradients.
A.4 ADDITIONAL TRAINING DETAILS
We use learning rates of 0.005, 0.005, 0.01 for albedo, roughness, and cubemap, respectively, and other hyperparameters following the configuration of 2DGS (Huang et al. 2024). We represent the optimizable environment map as cubemap with a resolution of 32. The first initailize stage is trained for 40K iterations, with loss weight hyperparameters Ad, An Anss X, as 1000, 0.05, 0.02, and 0.05, respectively. The inverse rendering stage trained for 20K iterations, with loss weight hyperparameters Äas, Xra, Atight 0.2, 0.1, and 0.01, respectively. After the initialization stage, we reinitialize the albedo, roughness, and cubemap. Then, we start the inverse rendering stage with the same learning rate depicted above. For the finetuning stage, we the same learning rate only for the spherical harmonics coefficients.
A.S RENDERING AND RELIGHTING WITH SPLIT-SUM APPROXIMATION
Split-sum approximation is a technique for efficiently computing indirect illumination in physically based rendering. By decomposing the complex specular BRDF integral into two separable terms on Eq. 3. it avoids the computational burden of Monte Carlo sampling while preserving visual fidelity.
15

---

Published as a conference paper at ICLR 2026
We divide the light transport into diffuse La and specular Lg components each and approximate the specular light transport as below:
Lo(wo) J,
N)dw; Li(wi) wo) N)dw
(20)
This precomputation allows the specular contribution to be efficiently estimated at runtime by sam- pling the pre-filtered environment map (using the reflection vector and roughness) and the BRDF LUT.
Diffuse radiance La is computed more directly as the product the surface's diffuse reflectance (albedo) and the total incoming diffuse light. The latter is also precomputed by convolving the environment map with a cosine lobe to create an irradiance map.
We apply the split-sum approximation for the initialization stage to easily approximate the estimate of physically-rendert rendered outgoing radiance Lybr on each Gaussian primitive. For relighting, we apply the split-sum approximation to calculate the incident indirect illumination Lind from the traced secondary ray using the ray-traced surface position, normal, albedo, and roughness values. The achieved Lind is used for relighting integrated with the traced visibility V and the queried direct light Ldir.
B COMAPARISON ON RELIGHTING PERFORMANCE AND RENDERING COST
On 3 we report relighting performance using three configurations: (1) Gaussian ray tracing that estimates indirect radiance using the PBR split-sum approximation (PBR split-sum), (2) Gaussian ray tracing that uses indirect radiance predicted by fine-tuned surfels (PBR fine-tuned), and (3) direct rasterization with fine-tuned surfel radiances (Surfel fine-tuned). While fine-tuning introduces slight quality drop, it enables the surfel radiances to adapt new lighting conditions and act as physically consistent indirect illumination sources, all while achieving substantially faster rendering speeds than competing approaches. We also provide qualitative comparisons between the three configurations in Figure 20
Table 3: Relighting Performance and Rendering cost during relighting on TensoIR dataset.
Method
Relight SSIM 1
Rendering ms
PSNR
LPIPS 1
IRGS
0.935
SVG-IR
31.10
0.946
0.056
82.48
PER split-sum
32.09
0.933
0.048
38.64
PBR linetuned
0.952
38.29
Surfel finetuned
31.41
0.948
0.052
5.902
C VISUAL COMPARISON ON ILLUMINATION COMPONENTS
We provide additional visual comparisons "hotdog" and the "lego" scene from the TensoIR dataset (Jin et al.) 2023) Figure 7 and Figure g We visualize illumination components including incident direct and indirect radiances. and their rendered results the datasets along with Gaussian- based methods IRGS (Gu et al.) (024)) and SVG-IR (Sun et al. 2025) to compare our realistic indirect illumination. The components are the mean value of the samples during the Monte Carlo rendering. Our method provides realistic indirect illumination that maintains the fine details of inter-reflecting surfaces, while the IRGS (Gu al. 2024) often overestimates the intensity of the indirect radiance and SVG-IR (Sun et al. 2025) often underestimates the intensity of indirect radiance due to the lack of physical guidance for indirect radiances unobserved views.
16

---

Published as a conference paper at ICLR 2026
IRGS
SVG-IR
Ours
Direct radiance
Direct render
Indirect radiance
Indirect
render
Figure 7: Qualitative comparison on illumination components the "hotdog" scene of TensoIR dataset. Best viewed in zoom.
17

---

Published as a conference paper at ICLR 2026
IRGS
SVG-IR
Ours
Direct radiance
Direct render
Indirect radiance
Indirect render
Figure 8: Qualitative comparison illumination components the"lego" scene of TensoIR dataset. Best viewed in zoom.
18

---

Published as a conference paper at ICLR 2026
D ADDITIONAL VISUAL COMPARISON ON BENCHMARK DATASETS
We provide additional visual comparisons on "armadillo" scene from the TensoIR Jin et al. 2023) dataset, and all the scenes from the Synthetic4Relight dataset from Figure 9 to For the TensoIR dataset, we deliver comparison
novel-view synthesis (NVS), normal reconstruction, albedo re- construction and relighting with Gaussian- based methods IRGS Guu et al. (024) and SVG-IR (Sun) et al. 2025), and NeRF-based method TensoIR (Jin et al. 2023). For the Synthetic4Relight dataset, we deliver comparison on novel-view synthesis (NVS), albedo reconstruction, roughness reconstruc- tion, and relighting with Caussian-basee methods R3DG (Gao et al. 2024). IRGS (Gu et al.
2024
and SVG-IR (Sun et al. 2025).
TensoIR
IRGS
SVG-IR
Ours
GT
NVS
Albedo
Normal
Relight
(fireplace)
Relight
(forest)
Figure 9: Qualitative comparison on NVS, albedo reconstruction, normal reconstruction, and re- lighting the "armadillo" scene of the TensolR dataset. Best viewed in zoom.
19

---

Published as a conference paper at ICLR 2026
R3DG
IRGS
SVG-IR
Ours
GT
NVS
Albedo
Roughness
Relight (env#6)
Relight (env # 12)
Figure 10: Qualitative comparison on NVS, albedo reconstruction, roughness reconstruction, and relighting the "jugs" scene of the Synthetic4Relight dataset. Best viewed in zoom.
20

---

Published as a conference paper at ICLR 2026
R3DG
IRGS
SVG-IR
Ours
GT
NVS
Albedo
Roughness
Relight (env#6)
Relight (env:# 12)
Figure II: Qualitative comparison on NVS, albedo reconstruction, roughness reconstruction, and relighting the "air baloons" scene of the Synthetic4Relight dataset. Best viewed in zoom.
21

---

Published as a conference paper at ICLR 2026
R3DG
IRGS
SVG-IR
Ours
GT
NVS
Albedo
Roughness
Relight (env#6)
Relight (euv:# 12)
Figure 12: Qualitative comparison on NVS, albedo reconstruction, roughness reconstruction, and relighting the "chair" scene of the Synthetic4Relight dataset. Best viewed in zoom.
22

---

Published as a conference paper at ICLR 2026
E EVALUATION OF INDIRECT ILLUMINATION
Current inverse rendering benchmarks, including TensoIR Jin et al. (003), Synthetic4Relight Zhang ctt al. (022)) and Stanford-ORD Kuang et al. (2023), not provide ground truth (GT) for indirect illumination for quantitative and qualitative evaluation. To address this limitation, we generated a new evaluation dataset with explicit GT Indirect Illumination. We utilized the original Blender files from the TensoIR dataset generate high high-fidelity ground truth. We used Blender Cycles path tracing engine to render the indirect illumination pass along with the original render pass. To ensure noise-free references, especially for indirect illumination, applied the OIDN denoiser [3]. This allows direct quantitative evaluation the indirect illumination components. We trained our model and the ablation model discarding the radiometric consistency loss (Ours w/o Lrad) using the same hyperparameters described in the paper. We also trained two baselines, IRGS and SVG-IR, for comparison.
set the sampling rate to 256 spp and
Table presents the quantitative comparison against baselines (IRGS, SVG-IR) and our ablation model on our new dataset. Our method significantly outperforms all baselines in indirect illumina- tion reconstruction, confirming that our method accurately models the physical transport of indirect light. Our accurate indirect illumination leads to superior performance in most other metries. When radiometric consistency is removed (Ours w/o Lrad), the indirect PSNR drops significantly, show- ing that the performance gain comes from our proposed framework utilizing radiometric consistency, which effectively supervises indirect radiance from unobserved views.
As shown, our method faithfully reconstructs indirect illumination compared to baseline models and our ablation model. Overall, our method pro- duces indirect illumination closest to the ground truth, while IRGS produces overestimated intensity, and SVG-IR tends underestimate the intensity of the indirect radiances. Similar phenomena can also be observed in the qualitative results in Figures and of our paper. Our ablation model (Ours w/o Grad) tends produce white blurs on inter-reflecting regions compared our method due to the lack of supervision on unseen views. Finally, we provide additional qualitative comparisons of indirect illumination during relighting in Figure 14, where our method produces more realistic, accurate indirect illumination than the baseline models.
We also provide qualitative comparisons Figure 13
Table 4: Quantitative comparison against baselines (IRGS, SVG-IR) and our ablation model our new dataset. Our method significantly outperforms all baselines in indirect illumination reconstruc- tion.
NVS
Indirect Illumination
Geometry
Alhedo
Method
PSNR
SSIM
LPIPS 4
PSNR f
SSIM
IPPPS
Normal MAF.J
PSNR
SSIM
I.IPS
IRGS
35.0982
0,9660
0.0436
24.2219
0,8792
0.1092
4.1835
30.0931
0.9521
0.0752
SVG-IR
36.9634
0.0266
30.9747
0.9134
0.0843
4.2624
29.7354
0.0822
Ours w/o Crod
36.3471
0.9764
0.0276
30.0954
0.9161
0.0752
3,8332
30,3425
0.9470
0.0760
Ours
37.8519
0.9822
0.0212
32,8832
0.9266
0.0726
3.6048
30.6224
0.9502
0.0744
23

---

Published as a conference paper at ICLR 2026
IRCS
SVC-IR
Our (w/o Fran)
Qurs
GT
NVS
Iodtiest Eltemination
NVS
Lattrest Eltamication
Sptines
NVS
Iadired
NVS
Indirect Iltmniaaiio
Figure 13: Qualitative comparison on novel-view synthesis and indirect illumination on our dataset. Best viewed in zoom.
24

---

Published as a conference paper at ICLR 2026
IRGS
SVG-IR
Omns
GT
Bridge
City
Forest
Night
Figure 14: Qualitative comparison on indirect illumination for four different lighting conditions on the "hotdog" scene of our dataset. Best viewed in zoom.
25

---

Published as a conference paper at ICLR 2026
F ABLATION STUDIES ON RADIOMETRIC CONSISTENCY
In this section, we discuss the contributions of the two main components of our framework, differ- entiable Gaussian ray tracing and supervision on unobserved (i.e. unseen) direction.
F.1 RADIOMETRIC CONSISTENCY AND DIFFERENTIABLE GAUSSIAN RAY TRACING
The primary challenge in applying radiometric consistency to existing GS-based inverse rendering that existing pipelines cannot dynamically query surfel radiances as indirect radiances during op- timization. Existing pipelines employ point-based ray tracing baked volumes, which precompute indirect radiance from NVS-pretrained Gaussian primitives in a non-differentiable manner. These precomputed values remain fixed during the optimization. When these values do not reflect updated Gaussian attributes, the supervision signal from radiometric consistency may become inconsistent. Our framework addresses this issue by employing differentiable Gaussian ray tracing to query indi- rect radiance for sampled surfels at every iteration.
We further conducted ablation studies to show how framework enhances the contribution of radiometric consistency in Gaussian-basee inverse rendering. Starting from the model initialized with our method, we trained three ablation models using different methods for querying indirect radiances and rendering, while utilizing our radiometric consistency loss during inverse rendering optimization.
"Split-sum": Applies split-sum approximation to calculate physically- radiance dur- ing the inverse rendering optimization, which does not involve indirect illumination.
"RT precompute": Applies Monte Carlo estimate (Eq.(8)) to calculate and precomputes indirect radiances via ray tracing, freezing the indirect illumination estimate during the optimization.
Dynamically updates indirect radiances via ray tracing during optimization, but without updating through ray-traced surfels by detaching the gradient on the ray-traced results.
"RT w/o diff.":
The quantitative results of the three ablation models and our method are depicted in Table "Split- sum" shows severe degradation in normal, albedo, and relighting accuracy due to the lack of illu- mination effects from surfels. "RT Precompute" shows enhanced normal, albedo, and relighting accuracy by utilizing precomputed indirect illumination, but yields the lowest novel-view synthe- sis performance among all methods. While "RT w/o Diff" improves overall performance through dynamic updates to these values, it still falls short of our method across all metrics. Ours method achieves the best performance across all metrics, demonstrating that the fully differentiable self- correcting feedback loop is essential for robust disentanglement.
Table 5: Ablation study on radiometric consistency strategies. We compare our method with baselines using different indirect illumination handling. Best results are highlighted in bold.
NVS SSIM
Geametry Nonnal MAE
Albedo SSIM
Relighting SSIM
Method
PSNR
LPIPS
PSNR
LPIPS
PSNR 1
LPIPS
Split-sum
36.659
0.9800
0.0254
4.0066
27.4
0.0828
28.816
0.9371
0.0575
RT Precompute
35.547
0.9728
0.0332
3.7138
30.523
0.9481
0.0750
31.965
0.9516
0.0496
RTT w/o Difi.
37.252
0.9789
0.0275
3.6946
30.673
0.9507
0.0732
32.050
0.9524
0.0484
Ours
37.858
0,9801
0.0266
3.6889
31.048
0,9523
0.0719
32,092
0,9533
0.0478
F.2 SUPERVISION ON UNOBSERVED DIRECTION
To further support our interpretation that the radiometric consistency supervises unseen directions, we conducted an additional ablation where we train both "Ours" and "Ours w/o Crad on only 50% and 25% of the randomly subsampled training views of our new dataset Appendix E The numerical results on NVS and indirect illumination reconstruction performance are in Table 6 We have denoted the performance drop relative to the full training view (100%) the right side of
the metrics. NVS metrics degrade for both methods when fewer views are used. However, when using 25% of the training views, indirect illumination reconstruction with model remains nearly
26

---

Published as a conference paper at ICLR 2026
unchanged (-0.17dB), whereas the ablation shows significant drop in indirect PSNR (-2.21dB). This indicates that when the camera viewpoint is limited, radiometric consistency still provides effective supervision that cannot be provided by NVS alone.
Table 6: Ablation study training view scarcity. We report NVS and Indirect PSNR metrics across different subsets of training views. Values in parentheses denote the performance drop relative the 100% setting.
NVS PSNR
Indirect PSNR
Train views
Ours
Ours w/o Load
Ours
Ours w/o Cra!
100%
37.85
36.35
32.88
30.10
37.54
35,81 (-0.54)
32.79 (-0.09)
29.15
25%
36.79 (-1.06)
34.65 (-1.70)
32.71 (-0.17)
27.89 (-2.21)
27

---

Published as a conference paper at ICLR 2026
G ABLATION STUDY ON HYPERPARAMETERS N, AND N
G.1 ANALYSIS ON TENSOIR DATASET
We evaluated the performance impact of two key hyperparameters on the TensolR dataset: the number of surfels sampled for radiometric consistency (Ng) and the number of incident ray samples (N₂) per surfel.
First, we varied N₂ from 1024 to 8192 while keeping N, fixed at 64, As shown in Table increasing Ng generally improves reconstruction quality across all tasks and metrics. This improvement is attributed to the radiometric consistency loss supervising a larger number of surfels per iteration. Notably, N₂ does not impact the rendering cost during inference, it strictly controls the number of surfels supervised during the optimization step.
Table 7: Ablation study on the number of surfels (N₂) for radiometric consistency. We vary No while fixing N₂ 64. Increasing Ng improves quality without affecting rendering cost.
NVS
Geumetry
Albedo
Relight
Render
N₂
PSNR 1
SSIM 1
LPIPS
Normal MAE
PSNR 1
SSIM 1
LPIPS
PSNR
SSIM
LPIPS
(ms)
1024
37.8206
0.9799
0.0268
3.6900
30.9137
0.9518
0.0730
32,0569
0.9529
0.0481
38.5
2048 (Ours)
37,8580
0.9801
0.0366
3,6889
31,0479
0.9521
0.0721
32.0920
0.9533
0.0478
38.6
4096
37,8707
0.9802
0.0264
3.6852
31,0495
0.9522
0.0721
32.1112
0.9532
0.0478
38.7
8192
37,8799
0.0263
31,0507
0.0720
32.1294
0.9533
0.0477
Next, analyzed the effect of N₂ ranging from 16 to 128 with N, fixed 2048. Table S demon- strates that reconstruction quality improves as N, increases up to 64. However, increasing N, fur- ther 128 yields diminishing returns, and in some tasks (e.g.. NVS and Relighting), performance slightly drops. This suggests that the additional ray samples beyond this point do not significantly resolve the variance in Monte Carlo integration for the given capacity. regarding efficiency, the ren- dering cost scales with N, due to the additional computation required for the Monte Carlo estimate of T. BR However, the rendering cost remains manageable even N, = 128, achieving 58.3 ms frame (~17.2 fps).
Table 8: Ablation study the number of secondary ray samples (N.). We vary No while fixing N₂ = 2048. Performance saturates around N, 64.
N.
NVS
Geometry
Albedo
Relight
Render
PSNR
SSIM
LPIPS
Normal MAE
PSNR
SSIM
LPIPS
PSNR
SSIM
LPIPS
(ms)
16
37,4199
0.9792
0.0275
3.7174
30.8656
0.9505
0.0735
32.1684
0.9518
0.0494
16.2
32
37,8125
0.9799
0.0268
3.6979
30.9014
0.9510
0.0730
32,1537
0.9528
0.0483
22.6
64 (Ours)
37,8707
0.9802
0.0264
3.6852
31.0479
0.9521
0.0721
32.1112
0.9532
0.0478
38.6
128
37.6548
0.9798
0.0268
3.6821
30,9563
0.9512
0.0728
31.9768
0.9526
0.0480
58.3
G.2 RENDERING EFFICIENCY ON LARGE-SCALE SCENES (MIPNERF360)
We further provide analysis MipNeRF360 (Barron et al. 2022) dataset to evaluate the rendering cost in complex, scene-level environments. Qualitative results corresponding to this analysis are provided in Figure [5]
We further investigated the computational cost by varying N, from 1024 to 16384 while keeping N. fixed at 64 (Table 19. Consistent with the analysis on the TensoIR dataset, varying N, has negligible impact on the rendering cost.
Finally, we analyzed the effect of the number of incident ray samples (N₂) rendering time, vary- ing N₂ from 16 to 64 with N, fixed at 2048 (Table 10P. As shown, the rendering cost scales with N.. This indicates that while larger scenes increase the baseline computational load, the rendering speed can be effectively controlled by adjusting N.. Although reducing N, improves speed, it may trade off rendering quality. However, given that increasing N, improves quality without computa- tional overhead, our method can scale to larger scenes while maintaining efficiency by strategically balancing N, and N..
28

---

Published as a conference paper at ICLR 2026
Table 9: Rendering cost analysis on MipNeRF360 with varying Ng We fix N, 64. Consistent with TensolR, Ng does not significantly affect rendering speed.
N₂
Average (ms)
Bonsai (ms)
Counter (ms)
Kitchen (ms)
Room (ms)
1024
81.23
77.37
72.38
86.81
88 48.43
(Ours)
81.40
77.78
72.95
84,29
90.56
4096
81.69
78.8
72.19
85,28
90.41
8192
81.53
77.75
73.34
85,22
89.82
16384
81.86
77.75
73.24
86.08
90.38
Table 10: Rendering cost analysis MipNeRF360 with varying No. We fix Ng 2048. Reducing N, significantly decreases rendering time, allowing for trade-offs between speed and quality.
N.
Average (ms)
Bonsai (ms)
Counter (ms)
Kitchen (ms)
Room (ms)
16
24.57
23.49
23.86
26.07
24.85
43.43
42.00
39.32
47.31
45.10
(Ours)
77.78
72.95
84.29
90.56
Bonsai
Counter
Kitchen
Room
Render
Normal
Albedo
Occlusion
Indirect
Ilmmination
Relighting1
Relighting2
Figure 15: Qualitative results on the MipNeRF360 dataset. Best viewed in zoom.
29

---

Published as a conference paper at ICLR 2026
H ADDITIONAL ANALYSIS ON THE INITIALIZATION STRATEGY
We compared our initialization strategy against NVS- initialization methods. We define "NVS Init." as a model utilizing NVS pre-training for initialization followed by our full inverse rendering optimization. Additionally, we evaluated "NVS Init. w/o Lrad", where the radiometric consistency loss is removed throughout both the initialization and the subsequent optimization stages.
Table I1 summarizes the quantitative results. The "NVS Init. w/o Lrad" baseline exhibits the lowest performance across all tasks, highlighting the necessity radiometric supervision. While introduc- ing radiometric consistency after NVS pre-training (NNVS Init.") improves albedo and relighting quality, it still falls short of our method. Notably, even when our method is trained without radio- metric consistency during the main optimization phase ("Ours w/o Lrad"), it outperforms the full NVS-initialized model in albedo reconstruction. This suggests that enforcing physical constraints from the start is crucial for robust disentanglement.
Table 11: Ablation study on initialization strategies. We compare our initialization (Ours) against WVS-based initialization baselines. "NVS init." denotes NVS pre- pre-training followed by our standard optimization. "during IR" refers to the inverse rendering optimization stage.
Method
NVS PSNR f
Albedo PSNR
Relight PSNR
NVS init. Arı during IR
35,70
30.31
31,51
NVS init.
37.43
30.34
31.66
Ours wlo Lead Onnd during IR)
35,82
30.82
31.69
Ours
37.86
31.05
32.09
We further analyze the convergence behavior and qualitative results in Figure T6 As shown in the convergence plot (left of Figure[T)), our initialization leads to substantially faster and more stable convergence of the radiometric consistency loss Lrad, whereas standard NVS initialization results in higher and noisier residuals. This demonstrates that conventional NVS initialization places the surfels into suboptimal orientation that is misaligned with the physical decomposition required for inverse rendering with radiometric consistency. Qualitatively (right of Figure [6). NVS pre-training causes the model to misinterpret geometric cues, leading shadows baked into the geometry (red arrows) and inter-reflections merged into the albedo (blue arrows). In contrast, our initialization preserves physical priors from the beginning, enabling enhanced geometry reconstruction and cleaner separation of inter-reflection effects.
frad convergence
NVS init
Ours init
GT
0.175
NVS
Ours ini.
Albedo
0.150
Normal
0.025
0.000
20000
Training Step
Figure 16: Ablation study of our initialization method on the "hotdog" scene of TensoIR dataset.
30

---

Published as a conference paper at ICLR 2026
I ENVIRONMENT MAP RECONSTRUCTION ON BENCHMARK DATASETS
We visualized the optimized environment maps from the TensoIR (Jin et al. 2023) and Syn- Rhelict (Zhang et al., 2022) datasets and compared them with baseline methods and Ground Truth (GT) in Figures 17 and 18 Our method consistently recovers environment maps that are visually closest to the ground truth environment map. reflecting the benefit of our radiomet- ric consistency, leading robust disentanglement of illumination for inverse rendering. In contrast, GI-GS and GS-IR show unnatural environment maps compared to other baseline methods. Baselines relying on point-based ray tracing (R3DG and SVG-IR) often fail disentangle base color from the environment map, such as for the scenes "hotdog" and "lego" from TensoIR, and "air balloons" from the Reelheti dataset. While IRGS uses differentiable Gaussian ray tracing, tends overestimate the intensity in regions that should be dark, such as the ground of the GT environment map.
GS-IR
GI-GS
R3DG
SVG-IR
IRGS
Oun
GT
Prent Armoo
Figure 17: Qualitative comparison on reconstructed environment map on TensoIR dataset. Best
viewed zoom.
R3DG
SVC-IR
IRCS
Ours
CT
Chuir
Air Ballons
Hoodog
Jugs
Figure 18: Qualitative comparison on reconstructed environment map on Synthetic4Relight dataset. Best viewed in zoom.
31

---

Published as a conference paper at ICLR 2026
J ADDITIONAL ANALYSIS ON FINETUNING-BASED RELIGHTING STRATEGY
We visualize the convergence and visual progression in Figure 19 As finetuning progresses, the image rendered by surfel radiances quickly converges towards the PBR reference (orange line, left plot). However, the quality of the PBR reference itself slightly degrades plot), due to the errors accumulated by the finetune surfel radiances that contribute as indirect il- lumination for physically-bor ances to new lighting conditions, allowing us to directly use learned surfel radiances to rasterize relighted frames. We additionally provide the corresponding visual comparisons Figure 20 of the revised paper, which demonstrates that our finetuning- based relighting method shows similar relighting quality compared to ray-traced results.
time (blue line, left
rendering. Nevertheless, our method quickly adapts surfel radi-
Convergence
Strp.1
(14)
3E0
12/0
1850
2000
Now Lighting coddttion
Relighting Performancc
Sunte PeBOOO
GT
730
403
Pinetuning Steps
Figure 19: Illustrative figure on how our finetuning-based relighting adapts surfel radiances for new lighting conditions.
PER render (Split-smm))
PER render (Finetuned)
Surfel render (Finctuned)
Relight GT
PBR render (Split-sum))
PBR render (Finetumed)
Surfel render (Finetuned)
Relight GT
Armedillo
Fleus
Hotdog
Lego
Figure 20: Qualitative ablation study on three relighting method: 1) Gaussian ray tracing that queries indirect radiance via split-sum approximation "PBR render (Split-sum)",, 2) Gaussian ray tracing that queries indirect radiance from finetuned surfels "PBR render (Finetuned))" and 3) direct rasterization with finetuned surfel radiances "Surfel render (Finetuned)".
While our proposed finetuning based relighting introduces a pre-computation step compared to ex- isting relighting pipelines, it offers advantages in terms of rendering cost and memory efficiency.
32

---

Published as a conference paper at ICLR 2026
We analyze the trade-off between additional finetuning cost and real-time capabilities by comparing average rendering speed and VRAM consumption on the TensoIR dataset. We compare our fine- tuned model against a baseline relighting method that utilizes Gaussian ray tracing for shading with varying sample counts N₂ E {64, 128, 256).
As reported in Table 12 although finetuning incurs upfront computational cost, it eliminates the necessity storing incident radiance for every surfel during inference. This results in significantly reduced memory usage and faster inference speeds (~5.90 ms). In contrast, baseline relighting methods demonstrate increasing rendering time and memory consumption proportional to the sam- ple count N, they require storing dense incident ray information for shading each surfel. These results suggest that our method is particularly suitable for remoryyconstrainge environments, such as consumer-grade GPUs or edge devices, where storing dense lighting data becomes prohibitive.
Table 12: Comparison of rendering speed and memory usage. We compare our finetuned model against the baseline relighting method with varying ray tracing sample counts (N). Our method achieves significantly lower rendering time and memory footprint.
Method
Sample Count (N)
Rendering (ms)
VRAM (MB)
Ours (Finetuned)
5.90
308.2
64
38.64
1512.6
Baseline (Ray Tracing)
58.31
2523.3
256
86.99
4589.5
33


Reviews:
[{"review_id": "40LbZ4lQsl", "reviewer": "Reviewer_MPqe", "summary": "This paper tackles the problem of inverse rendering with 3D Gaussian Splatting, specifically focusing on the challenge of accurately modeling indirect illumination. Existing methods often struggle to disentangle material properties from global illumination effects because they lack supervision for indirect radiance from unobserved viewpoints. To this end, the paper proposes Radiometrically Consistent Gaussian Surfels (RadioGS), an inverse rendering framework that integrates radiometric consistency using 2D Gaussian ray tracing to efficiently compute indirect illumination and visibility.", "questions": "1. See W1. Is the radiometric consistency a form of optimization where a learned or cached representation of global illumination is regularized to match a physically-based render?\n2. What is the codebase for implementation? Currently, it seems that the method is built upon IRGS's implementation.", "limitations": "", "rating": 2, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "The paper is well-written. The paper demonstrates state-of-the-art performance on two standard benchmarks (TensoIR, Synthetic4Relight). The quantitative tables and qualitative figures (especially the superior handling of inter-reflections in Figures 1 and 5) convincingly show the benefits of the proposed approach over existing methods. The proposed finetuning-based relighting method is very fast (<10ms), making the framework practical for applications requiring real-time rendering under dynamic illumination.", "weaknesses": "1. A primary concern is the conceptual framing of \"radiometric consistency\". The paper presents this as a \"novel physical constraint\", but its fundamental distinction from standard methods for computing indirect illumination, such as path tracing, is unclear. The underlying physical constraint is simply the rendering equation. This method appears to be a form of optimization where a learned or cached representation of global illumination is regularized to match a physically-based render. If this interpretation is correct, then radiometric consistency is not a new physical principle but rather an efficient approximation or caching strategy designed to enforce it. The novelty would then lie in the caching technique itself, not the underlying physics.\n\n2. The formulation of the indirect radiance term, $L_\\text{ind}$, is critically underspecified. Despite being a cornerstone of the proposed method, the paper lacks a clear mathematical definition for how $L_\\text{ind}$ is computed. The text refers readers to Eq. 4 and Eq. 5 (line 240), but this reference is confusing and unhelpful. Eq. 4 defines the BRDF, and Eq. 5 merely decomposes incoming radiance into direct and indirect components without providing a computational model for the latter. The method's implementation for this crucial term is left to textual descriptions (e.g., lines 240, 268), which is insufficient for a clear understanding and reproduction of the work.\n\n3. The evidence supporting the central claim of improved indirect illumination modeling is indirect and appears weak. The paper validates its method via ablation studies on end-to-end metrics (e.g., NVS, albedo PSNR) across a limited set of scenes. However, these final metrics can be influenced by many factors in the rendering pipeline. Without a direct, quantitative evaluation of the indirect illumination component itself—for instance, by comparing the rendered indirect pass against a ground-truth path-traced equivalent—it is difficult to conclude that the performance gains stem specifically from more accurate indirect radiance estimation. Relying on proxy metrics from a small number of scenes makes the current conclusion insufficiently substantiated.", "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": "WGsmCcbv2t", "reviewer": "Reviewer_mzwa", "summary": "This paper tackles the key problem of poor indirect illumination modeling in Gaussian Splatting-based inverse rendering. It proposes a physical constraint called radiometric consistency, designs an inverse rendering framework named RadioGS, and develops a fast finetuning-based relighting strategy. The radiometric consistency constraint provides supervision for unobserved viewpoints by minimizing the residual between the learned radiance of Gaussian surfels and their physically rendered radiance. This forms a self-correcting feedback loop. The RadioGS framework integrates 2D Gaussian ray tracing to deploy the constraint efficiently. The relighting strategy can adapt to new illumination conditions in minutes, with rendering latency below 10 ms. Experiments show the method outperforms existing Gaussian-based methods in novel view synthesis, geometry and material reconstruction, and relighting.", "questions": "1. Scaling the method to scene-level settings presents primary challenges beyond the current object-centric experiments. Could the authors discuss the anticipated computational bottleneck in such a scenario? A detailed analysis of how the sampling parameters (e.g., N_g, N_s) and the associated rendering time are expected to scale would be crucial for assessing the method's practicality.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. The paper is intuitive and easy to follow.\n2. By combining the surfel radiance represented by spherical harmonics with physically based rendered radiance, the idea is straightforward and effective.\n3. Through carefully designed loss functions, path tracing, and multi-stage training, the geometry reconstruction and inverse rendering have achieved superior results on multiple datasets.", "weaknesses": "1. The experimental validation is conducted exclusively at the object level, lacking evaluation on complex scene-level datasets. The scalability of the proposed framework to scene-level settings is a significant concern. The core Monte Carlo sampling strategy, which is computationally intensive even for objects, would likely incur a prohibitive overhead when applied to large-scale scenes with a massive number of Gaussian surfels.\n2. The performance of the inverse rendering framework is dependent on the quality of a pre-trained novel view synthesis model, introducing a potential dependency and a point of failure.", "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": "YdcMRqyxoe", "reviewer": "Reviewer_47iG", "summary": "This paper presents a novel inverse rendering framework, RadioGS, which better modeling the complex material-lighting interaction by utilizing a novel radiometric consistency loss. The proposed radiometric consistency loss are built on the 2D Gaussian Ray Tracing and enables more accurate indirect illumination querying. Experiment results are provided to demonstrate the effectiveness of the framework, highlighting the effectiveness of the novel loss.", "questions": "1. What does the optimized envmap looklike, as the indirect illumination is more accurate, the estimated direct illumination should also be better.\n2. Why can a simple finetune make the surfel’s outgoing radiance adopt to new lighting conditions? It sounds magic~", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The paper propose a novel inverse rendering framework where 2DGRT are used to calculate the rendering equation and radiometric consistency loss, which achieve accurate inter-reflection modeling and material estimation\n\n2. The proposed initialization and finetune-based relighting schemes further improve the inverse rendering and relighting performance.\n\n3. Extensive experiments show that the method achieve best performance over inverse rendering baselines.", "weaknesses": "1. The proposed loss ensures the consistency between the surfel’s outgoing radiance and the PBR result. But it cannot promise that the outgoing radiance $L_G$ is correct in unseen direction. So I'm curious why it can improve the inverse rendering performance.\n2. As we are talking about inverse rendering, so the material estimation should be more important than NVS. But according to the albedo PSNR shown in Fig.6, it seems that the performance gain comes more from NVS init. This will weaken the core contribution of the paper.", "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": "Nc3laCbaw0", "reviewer": "Reviewer_ShdJ", "summary": "The paper proposes a physically grounded inverse rendering framework called RadioGS. It introduces radiometric consistency, a physical constraint that enforces the agreement between each Gaussian surfel’s learned radiance and its physically-based rendered counterpart. This self-correcting mechanism provides supervision for unobserved views, enabling accurate modeling of indirect illumination and inter-reflections. Built on 2D Gaussian ray tracing, RadioGS integrates this constraint efficiently to improve decomposition of geometry, material, and lighting. Moreover, it includes a finetuning-based relighting strategy that adapts surfel radiances to new lighting conditions within minutes while maintaining real-time rendering speed (<10 ms). Experiments on benchmark datasets demonstrate that RadioGS achieves superior inverse rendering and relighting quality over previous Gaussian-based and NeRF-based methods", "questions": "See weakness.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The introduction of radiometric consistency provides principled physical supervision for unobserved views.\n\n2. The proposed finetuning-based relighting method allows adaptation to new lighting conditions within minutes, achieving high-quality results with less than 10 ms rendering time per frame.\n\n3. Extensive experiments on multiple benchmarks (TensoIR, Synthetic4Relight, Stanford-ORB) demonstrate superior performance in both inverse rendering and relighting tasks compared to prior Gaussian and NeRF-based methods.", "weaknesses": "1.The contribution may be limited in novelty, the proposed radiometric consistency mainly enforces agreement between the surfel’s learned radiance and its physically-based rendered results, which is a relatively straightforward physical constraint.\n\n2.The relighting process requires additional finetuning under the proposed constraint, which introduces extra computation before rendering and reduces its advantage in fully real-time applications.\n\n3.Although the paper claims that the finetuning stage improves both realism and efficiency of relighting, the experiments lack an ablation study that explicitly compares relighting quality with and without the finetuning stage and also the physically-based rendering component.", "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 (Oral)