SSR-GS: Separating Specular Reflection in Gaussian Splatting for Glossy Surface Reconstruction
#SSR-GS #specular reflection #Gaussian splatting #glossy surfaces #3D reconstruction #rendering #computer vision
📌 Key Takeaways
- SSR-GS is a new method for 3D reconstruction of glossy surfaces using Gaussian splatting.
- It separates specular reflection from diffuse components to improve accuracy.
- The technique enhances rendering quality for reflective materials in computer graphics.
- It addresses challenges in reconstructing surfaces with complex light interactions.
📖 Full Retelling
arXiv:2603.05152v1 Announce Type: cross
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 prefilt
🏷️ Themes
Computer Graphics, 3D Reconstruction
📚 Related People & Topics
Gaussian splatting
Volume rendering technique
Gaussian splatting is a volume rendering technique that deals with the direct rendering of volume data without converting the data into surface or line primitives. The technique was originally introduced as splatting by Lee Westover in the early 1990s. This technique was revitalized and exploded in ...
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Connections for Gaussian splatting:
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Original Source
--> Computer Science > Computer Vision and Pattern Recognition arXiv:2603.05152 [Submitted on 5 Mar 2026] Title: SSR-GS: Separating Specular Reflection in Gaussian Splatting for Glossy Surface Reconstruction Authors: Ningjing Fan , Yiqun Wang View a PDF of the paper titled SSR-GS: Separating Specular Reflection in Gaussian Splatting for Glossy Surface Reconstruction, by Ningjing Fan and 1 other authors View PDF HTML 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 that couple a reflection-aware visual prior via a reflection score 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. Comments: Project page: this https URL Subjects: Computer Vision and Pattern Recognition (cs.CV) ; Artificial Intelligence (cs.AI cs.GR) Cite as: arXiv:2603.05152 [cs.CV] (or arXiv:2603.05152v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2603.05152 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Ningjing Fan [ view email ] [v1] Thu, 5 Mar 2026 13:24:13 UTC (3,748 KB) Full-text links: Access Paper: View a PDF of the paper titled SSR-GS:...
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