PolGS++: Physically-Guided Polarimetric Gaussian Splatting for Fast Reflective Surface Reconstruction

Yufei Han; Chu Zhou; Youwei Lyu; Qi Chen; Si Li; Boxin Shi; Yunpeng Jia; Heng Guo

cs.CVMar 11, 2026

PolGS++: Physically-Guided Polarimetric Gaussian Splatting for Fast Reflective Surface Reconstruction

Authors: Yufei Han, Chu Zhou, Youwei Lyu, Qi Chen, Si Li, Boxin Shi, Yunpeng Jia, Heng Guo,

Organizations: Beijing University of Posts and Telecommunications, China · National Institute of Informatics, Japan · vivo BlueImage Lab, vivo Mobile Communication Co., Ltd., Shanghai, China · State Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University, China ·

Abstract

Accurate reconstruction of reflective surfaces remains a fundamental challenge in computer vision, with broad applications in real-time virtual reality and digital content creation. Although 3D Gaussian Splatting (3DGS) enables efficient novel-view rendering with explicit representations, its performance on reflective surfaces still lags behind implicit neural methods, especially in recovering fine geometry and surface normals. To address this gap, we propose PolGS++, a physically-guided polarimetric Gaussian Splatting framework for fast reflective surface reconstruction. Specifically, we integrate a polarized BRDF (pBRDF) model into 3DGS to explicitly decouple diffuse and specular components, providing physically grounded reflectance modeling and stronger geometric cues for reflective surface recovery. Furthermore, we introduce a depth-guided visibility mask acquisition mechanism that enables angle-of-polarization (AoP)-based tangent-space consistency constraints in Gaussian Splatting without costly ray-tracing intersections. This physically guided design improves reconstruction quality and efficiency, requiring only about 10 minutes of training. Extensive experiments on both synthetic and real-world datasets validate the effectiveness of our method.

Figures & tables

Method	Input	Reflective	Type	Accuracy	Time
NeRO ( Liu et al., 2023 )	RGB Images	✓	SDF	high	8h
MVAS ( Cao et al., 2023 )	Azimuths	✓	SDF	medium	11h
PANDORA ( Dave et al., 2022 )	Pol. Images	✓	SDF	medium	10h
NeRSP ( Han et al., 2024 )	Pol. Images	✓	SDF	high	10h
PISR ( Chen et al., 2024b )	Pol. Images	✓	SDF	medium	0.5h
GS-ROR ( Zhu et al., 2025 )	RGB Images	✓	SDF+GS	medium	1.5h

Table 1: Comparison of different methods in reflective surface reconstruction on real-world datasets ( Dave et al., 2022 ; Chen et al., 2024b ; Han et al., 2024 ) with the image size

512*612

Figure 2: Comparison between SDF-based and 3DGS-based method on geometry representation. ( Top ) The surface normal of a point has a strong relationship with its opacity in NeuS ( Wang et al., 2021 ) . ( Bottom ) The surface normal of a point has no direct dependence on its opacity in Gaussian Surfels ( Dai et al., 2024 ) .

Figure 3: Pipeline of PolGS++. We re-rendered Stokes vectors $\hat{s}$ by using the diffuse color $C$ from 3DGS and specular color $L_{r}$ from Cubemap encoder module, which is supervised by the ground truth Stokes information. Tangent-space consistency constrain is applied to the surface normal estimated by 3DGS with the depth-guided visibility mask and AoP information.

Figure 4: Comparison of visibility mask acquisition strategies between SDF-based methods and our method based on Gaussian Splatting.

Figure 5: Visualization of visibility masks of one current view in SDF-based networks and in PolGS++.

	Hedgehog		Squirrel		Snail		David		Mean
Method	MAE	CD	MAE	CD	MAE	CD	MAE	CD	MAE	CD	time
PANDORA ( Dave et al., 2022 )	9.41	9.50	10.85	5.88	8.08	10.97	14.75	4.88	10.77	7.81	10 h
NeRSP ( Han et al., 2024 )	8.94	6.57	8.23	3.02	5.56	3.72	15.38	4.18	9.53	4.37	10 h
MVAS ( Cao et al., 2023 )	4.30	4.22	6.10	3.73	3.30	7.87	8.47	3.21	5.54	4.76	11 h
GS-ROR ( Zhu et al., 2025 )	9.72	4.45	9.25	4.20	7.24	5.16	14.02	4.14	10.06	4.49	1.5 h
NeRO ( Liu et al., 2023 )	3.40	3.69	3.55	1.86	2.67	3.71	7.64	2.88	4.32	3.04	8 h

Table 2: Comparisons on synthetic dataset (SMVP3D ( Han et al., 2024 ) ) evaluated by mean angular error (MAE) (

\downarrow

) with degree and Chamfer distance (CD) (

\downarrow

) in millimeter (mm), respectively. Best and second results in SDF-based and 3DGS-based methods (except PolGS ( Han et al., 2025 ) ) are highlighted as 1st and 2nd . The time consumed is shown on the far right side of the table.

	RMVP3D ( Han et al., 2024 )				PISR ( Chen et al., 2024b )
	Shisa		Frog		L-Rabbit		S-Rabbit		Mean
Method	MAE	CD	MAE	CD	MAE	CD	MAE	CD	MAE	CD	time
PANDORA ( Dave et al., 2022 )	12.93	11.29	15.86	7.88	13.47	16.53	11.45	15.39	13.43	12.77	10 h
NeRSP ( Han et al., 2024 )	10.79	7.39	15.62	6.68	13.33	7.43	15.75	10.27	13.87	7.94	10 h
MVAS ( Cao et al., 2023 )	8.56	9.28	17.63	7.00	11.86	13.82	12.81	12.08	12.72	10.55	11 h
PISR ( Chen et al., 2024b )	8.23	6.71	16.79	5.89	11.23	10.31	12.18	8.49	12.11	9.20	0.5 h

Table 3: Comparisons on real-world datasets (RMVP3D ( Han et al., 2024 ) and PISR ( Chen et al., 2024b ) ) evaluated by mean angular error (MAE) (

\downarrow

) with degree and Chamfer distance (CD) (

\downarrow

Figure 6: Qualitative comparisons on surface normal in SMVP3D ( Han et al., 2024 ) , where our method can outperform existing methods based on the same representation and achieves comparable results with SDF-based methods.

Figure 7: Qualitative comparison shape on PANDORA ( Dave et al., 2022 ) , RMVP3D ( Han et al., 2024 ) and PISR ( Chen et al., 2024b ) , where PolGS produces similar quality of reconstruction mesh compared to SDF-based methods.

Figure 8: Quantitative ablation results on SMVP3D ( Han et al., 2024 ) , where we evaluate the effect of polarimetric supervision under different input views.

Figure 9: Qualitative ablation results on PISR ( Chen et al., 2024b ) , where we compare the reconstruction quality with and without polarimetric supervision.

Figure 10: Ablation results on the visibility mask threshold.

Figure 11: Separation of diffuse and specular components with PANDORA ( Dave et al., 2022 ) , 3DGS-DR ( Ye et al., 2024 ) and PolGS++.

3D Reconstruction

3D reconstruction recovers a scene’s shape, appearance, and camera motion from images, video, depth, or other sensor measurements.

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PhyGaP: Physically-Grounded Gaussians with Polarization Cues

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The University of Hong Kong · Zhejiang University

3D Scene Relighting 3D Gaussian Splatting Physically Based Rendering Inverse Rendering

Open paper AskPDF arXiv

Recent advances in 3D Gaussian Splatting (3DGS) have demonstrated great success in modeling reflective 3D objects and their interaction with the environment via deferred rendering (DR). However, existing methods often struggle with correctly reconstructing physical attributes such as albedo and reflectance, and therefore they do not support high-fidelity relighting. Observing that this limitation stems from the lack of shape and material information in RGB images, we present PhyGaP, a physically-grounded 3DGS method that leverages polarization cues to facilitate precise reflection decomposition and visually consistent relighting of reconstructed objects. Specifically, we design a polarimetric deferred rendering (PolarDR) process to model polarization by reflection, and a self-occlusion-aware environment map building technique (GridMap) to resolve indirect lighting of non-convex objects. We validate on multiple synthetic and real-world scenes, including those featuring only partial polarization cues, that PhyGaP not only excels in reconstructing the appearance and surface normal of reflective 3D objects (~2 dB in PSNR and 45.7% in Cosine Distance better than existing RGB-based methods on average), but also achieves state-of-the-art inverse rendering and relighting capability. Our code will be released soon.

Abstract

3D Reconstruction

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