SSR-GS: Separating Specular Reflection in Gaussian Splatting for Glossy Surface Reconstruction

Ningjing Fan; Yiqun Wang

cs.CVMar 5, 2026

SSR-GS: Separating Specular Reflection in Gaussian Splatting for Glossy Surface Reconstruction

Authors: Ningjing Fan, Yiqun Wang

Organizations: Chongqing University, Choingqing, China

Abstract

In recent years, 3D Gaussian splatting (3DGS) has achieved remarkable progress in novel view synthesis. However, accurately reconstructing glossy surfaces under complex illumination remains challenging, particularly in scenes with strong specular reflections and multi-surface interreflections. To address this issue, we propose SSR-GS, a specular reflection modeling framework for glossy surface reconstruction. Specifically, we introduce a prefiltered Mip-Cubemap to model direct specular reflections efficiently, and propose an IndiASG module to capture indirect specular reflections. Furthermore, we design Visual Geometry Priors (VGP) that couple a reflection-aware visual prior via a reflection score (RS) to downweight the photometric loss contribution of reflection-dominated regions, with geometry priors derived from VGGT, including progressively decayed depth supervision and transformed normal constraints. Extensive experiments on both synthetic and real-world datasets demonstrate that SSR-GS achieves state-of-the-art performance in glossy surface reconstruction.

Figures & tables

Figure 1: Surface reconstruction results on toaster. CD denotes the Chamfer distance.

Figure 2: An overview of our SSR-GS pipeline. We rasterize 3D Gaussians to obtain surface buffers including normals, depth, opacity, diffuse component $C_{\mathrm{diff}}$ , roughness, $F_{0}$ , and alpha, and supervise them with geometry priors (GP). After rasterization, we extract a mesh via TSDF fusion and perform mesh-based ray tracing to estimate visibility $w_{\mathrm{vis}}$ . Direct specular reflection is queried from a Mip-Cubemap environment map, while indirect specular reflection is modeled by IndiASG. Finally, we apply physically based deferred rendering (Eq. 8 ) to produce the rendered image. We additionally compute the visual prior (VP) (reflection score) and use it to down-weight reflection-dominated pixels in the photometric loss for stable geometry initialization.

	ball	car	coffee	helmet	teapot	toaster	mean
Ref-NeRF [ 27 ]	1.55	14.93	12.24	29.48	9.23	42.87	18.38
ENVIDR [ 21 ]	0.74	7.10	9.23	1.66	2.47	6.45	4.61
UniSDF [ 28 ]	0.45	6.88	8.00	1.72	2.80	6.45	8.71
PGSR [ 2 ]	66.93	4.62	2.91	6.01	1.01	15.31	16.13
MILo [ 10 ]	61.30	4.47	2.36	4.87	0.62	16.49	15.02
GaussianShader [ 14 ]	7.03	14.05	14.93	9.33	7.17	13.08	10.93

Table 1: Quantitative surface reconstruction comparison on the ShinySynthetic dataset (normal MAE). CD is omitted since invisible inner surfaces in the GT meshes make it unreliable, as discussed in Ref-NeuS [ 8 ] for this dataset. Best results are highlighted in red and second-best in yellow.

CD $\downarrow$
	angel	bell	cat	horse	luyu	potion	tbell	teapot	mean
PGSR [ 2 ]	0.77	3.08	3.39	0.90	1.16	3.99	4.59	4.57	2.81
MILo [ 10 ]	0.77	16.52	2.84	1.02	44.64	3.39	5.08	4.40	9.83
GaussianShader [ 14 ]	0.85	1.10	2.56	0.73	1.07	4.74	5.74	3.40	2.53
Ref-Gaussian [ 37 ]	0.45	0.70	1.68	0.64	0.88	0.81	0.59	1.01	0.95
Ref-GS [ 42 ]	0.41	0.74	1.73	0.47	0.89	1.05	0.52	0.88	0.84

Table 2: Quantitative surface reconstruction comparison on the GlossySynthetic dataset (CD

\times 10^{2}

and normal MAE). Best results are highlighted in red and second-best in yellow.

	Mip-Cubemap	IndiASG	VGP
Stage 1	$\checkmark$		$\checkmark$
Stage 2	$\checkmark$	$\checkmark$

Table 3: Two-stage strategy.

Figure 3: Qualitative results of surface reconstruction on ShinySynthetic (car, coffee) and GlossySynthetic (cat, bell, teapot) datasets. Since the released GS-ROR 2 code cannot extract meshes on the ShinySynthetic, we instead visualize its normal results for comparison.

Figure 4: Qualitative results of surface reconstruction on Ref-Real (gardenspheres, toycar) dataset.

	Mip-Cubemap		IndiASG	VGP					Full
	w/o Mip	w/o Cube	w/o IASG	w/o VGP	w/o VP	w/o GP	w/o GP-D	w/o GP-N	Model
MAE $\downarrow$	1.56	1.62	1.52	2.25	1.55	2.15	1.53	2.86	1.52
MAE $\downarrow$	2.07	2.17	2.06	2.78	2.07	2.74	2.05	2.72	2.05
CD $\downarrow$	0.66	0.69	0.64	0.97	0.66	0.96	0.66	0.96	0.60

Table 4: Ablation study on different components. The first row corresponds to the ShinySynthetic dataset, while the remaining rows to the GlossySynthetic dataset.

Figure 5: Ablation study on different components.

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The reflective appearance, especially strong and typically near-field specular reflections, poses a fundamental challenge for accurate surface reconstruction and novel view synthesis. Existing Gaussian splatting methods either fail to model near-field specular reflections or rely on explicit ray tracing at substantial computational cost. We present Ref-DGS, a reflective dual Gaussian splatting framework that addresses this trade-off by decoupling surface reconstruction from specular reflection within an efficient rasterization-based pipeline. Ref-DGS introduces a dual Gaussian scene representation consisting of geometry Gaussians and complementary local reflection Gaussians that capture near-field specular interactions without explicit ray tracing, along with a global environment reflection field for modeling far-field specular reflections. To predict specular radiance, we further propose a lightweight, physically-aware specular adaptive mixing shader that fuses global and local specular features. Experiments demonstrate that Ref-DGS achieves state-of-the-art performance on reflective scenes while training substantially faster than ray-based Gaussian methods.

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