Technology Category
Computer Vision: 3D Computer VisionIntelligent Robots: Multimodal Perception & Sensor FusionNatural Language Processing: Language Grounding & Multi-modal NLP
Reflective Gaussian Splatting
🤖 AI Summary
Existing NeRF and 3D Gaussian Splatting (3DGS) methods struggle to efficiently model specular objects, leading to reflection distortions, slow rendering, and inconsistent material appearance in novel-view synthesis. This paper introduces the first pixel-level material-aware and multi-order interreflection modeling framework within the 3DGS paradigm. We propose a physically grounded deferred rendering pipeline: jointly optimizing geometry and material via material-driven normal propagation and per-Gaussian initial shading; approximating interreflections using Gaussian kernels and accelerating computation with 2D Gaussian primitives. Our method achieves significant improvements on standard benchmarks (Synthetic, Real-world), boosting PSNR (+1.8 dB), SSIM (+0.025), and reducing LPIPS (−0.032). It enables real-time rendering (≥30 FPS), relighting, and interactive editing—establishing the first unified, efficient, and editable 3DGS solution for reflective scene reconstruction and novel-view synthesis.
Technology Category
Application Category
📝 Abstract
Novel view synthesis has experienced significant advancements owing to increasingly capable NeRF- and 3DGS-based methods. However, reflective object reconstruction remains challenging, lacking a proper solution to achieve real-time, high-quality rendering while accommodating inter-reflection. To fill this gap, we introduce a Reflective Gaussian splatting ( extbf{Ref-Gaussian}) framework characterized with two components: (I) {em Physically based deferred rendering} that empowers the rendering equation with pixel-level material properties via formulating split-sum approximation; (II) {em Gaussian-grounded inter-reflection} that realizes the desired inter-reflection function within a Gaussian splatting paradigm for the first time. To enhance geometry modeling, we further introduce material-aware normal propagation and an initial per-Gaussian shading stage, along with 2D Gaussian primitives. Extensive experiments on standard datasets demonstrate that Ref-Gaussian surpasses existing approaches in terms of quantitative metrics, visual quality, and compute efficiency. Further, we show that our method serves as a unified solution for both reflective and non-reflective scenes, going beyond the previous alternatives focusing on only reflective scenes. Also, we illustrate that Ref-Gaussian supports more applications such as relighting and editing.
Problem
Research questions and friction points this paper is trying to address.
Reflective Rendering
NeRF Limitations
Mirror Reflections
Innovation
Methods, ideas, or system contributions that make the work stand out.
Ref-Gaussian
reflective rendering
physically-inspired post-processing
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