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arXiv:2512.10095 · v2

ReflFlow: Learning Geometry-Guided Ray Tracing for Dynamic Specular Reconstruction

首次提交 2025-12-11 05:36
版本更新 2026-11-12 07:08
雷达观测 2026-11-13 14:16
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摘要

We present ReflFlow, a novel framework for high-fidelity rendering of dynamic specular scenes by addressing two key challenges: precise reflection direction estimation and physically accurate modeling. To achieve this, we propose a Residual Material-Augmented 2D Gaussian Splatting representation that models dynamic geometry and material properties, allowing accurate reflection ray computation. Furthermore, we introduce a Dynamic Environment Gaussian and a hybrid rendering pipeline that decomposes rendering into diffuse and specular components, enabling physically informed specular synthesis via rasterization and ray tracing. Finally, we devise a coarse-to-fine training strategy to improve optimization stability and promote physically meaningful decomposition. Extensive experiments on dynamic scene benchmarks demonstrate that ReflFlow outperforms prior methods quantitatively and qualitatively, producing sharper and more realistic specular reflections in complex dynamic environments.

作者备注:ICML 2026. 16 pages, 6 figures, 5 tables, including references and appendices

期刊信息:Proceedings of the 43rd International Conference on Machine Learning, PMLR 306:119643-119658, 2026

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v2 · 2026-11-12 07:08 · 最新观测版本

ReflFlow: Learning Geometry-Guided Ray Tracing for Dynamic Specular Reconstruction

We present ReflFlow, a novel framework for high-fidelity rendering of dynamic specular scenes by addressing two key challenges: precise reflection direction estimation and physically accurate modeling. To achieve this, we propose a Residual Material-Augmented 2D Gaussian Splatting representation that models dynamic geometry and material properties, allowing accurate reflection ray computation. Furthermore, we introduce a Dynamic Environment Gaussian and a hybrid rendering pipeline that decomposes rendering into diffuse and specular components, enabling physically informed specular synthesis via rasterization and ray tracing. Finally, we devise a coarse-to-fine training strategy to improve optimization stability and promote physically meaningful decomposition. Extensive experiments on dynamic scene benchmarks demonstrate that ReflFlow outperforms prior methods quantitatively and qualitatively, producing sharper and more realistic specular reflections in complex dynamic environments.

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