TraceFlow: Dynamic 3D Reconstruction of Specular Scenes Driven by Ray Tracing
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915668096450560 |
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| author | Tao, Jiachen Wu, Junyi Wang, Haoxuan Yang, Zongxin Cai, Dawen Yan, Yan |
| author_facet | Tao, Jiachen Wu, Junyi Wang, Haoxuan Yang, Zongxin Cai, Dawen Yan, Yan |
| contents | We present TraceFlow, a novel framework for high-fidelity rendering of dynamic specular scenes by addressing two key challenges: precise reflection direction estimation and physically accurate reflection 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 grounded 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 TraceFlow outperforms prior methods both quantitatively and qualitatively, producing sharper and more realistic specular reflections in complex dynamic environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_10095 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TraceFlow: Dynamic 3D Reconstruction of Specular Scenes Driven by Ray Tracing Tao, Jiachen Wu, Junyi Wang, Haoxuan Yang, Zongxin Cai, Dawen Yan, Yan Computer Vision and Pattern Recognition We present TraceFlow, a novel framework for high-fidelity rendering of dynamic specular scenes by addressing two key challenges: precise reflection direction estimation and physically accurate reflection 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 grounded 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 TraceFlow outperforms prior methods both quantitatively and qualitatively, producing sharper and more realistic specular reflections in complex dynamic environments. |
| title | TraceFlow: Dynamic 3D Reconstruction of Specular Scenes Driven by Ray Tracing |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.10095 |