PRTGS: Precomputed Radiance Transfer of Gaussian Splats for Real-Time High-Quality Relighting
Yijia Guo, Yuanxi Bai, Liwen Hu, Ziyi Guo, Mianzhi Liu, Yu Cai, Tiejun Huang, Lei Ma
摘要
We proposed Precomputed Radiance Transfer of Gaussian Splats (PRTGS), a real-time high-quality relighting method for Gaussian splats in low-frequency lighting environments that captures soft shadows and interreflections by precomputing 3D Gaussian splats' radiance transfer. Existing studies have demonstrated that 3D Gaussian splatting (3DGS) outperforms neural fields in efficiency for dynamic lighting scenarios. However, the current relighting method based on 3DGS is still struggling to compute high-quality shadow and indirect illumination in real time for dynamic light, leading to unrealistic rendering results. We solve this problem by precomputing the expensive transport simulations required for complex transfer functions like shadowing, the resulting transfer functions are represented as dense sets of vectors or matrices for every Gaussian splat. We introduce distinct precomputing methods tailored for training and rendering stages, along with unique ray tracing and indirect lighting precomputation techniques for 3D Gaussian splats to accelerate training speed and compute accurate indirect lighting related to environment light. Experimental analyses demonstrate that our approach achieves state-of-the-art visual quality while maintaining competitive training times and importantly allows high-quality real-time (30+ fps) relighting for dynamic light and relatively complex scenes at 1080p resolution.
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引用它的顶会 Paper7
- RTR-GS: 3D Gaussian Splatting for Inverse Rendering with Radiance Transfer and ReflectionYongyang Zhou, Fanglue Zhang, Zichen Wang, Lei ZhangACM MM 2025 · 被引用 4 次
- RT-Splatting: Joint Reflection-Transmission Modeling with Gaussian SplattingJi Shi, Xianghua Ying, Bowei Xing, Ruohao Guo 等CVPR 2026 · 被引用 2 次
- SpikeGS: Reconstruct 3D Scene Captured by a Fast-Moving Bio-Inspired CameraYijia Guo, Liwen Hu, Yuanxi Bai, Jiawei Yao 等AAAI 2025 · 被引用 2 次
- Radiometrically Consistent Gaussian Surfels for Inverse RenderingKyu Beom Han, Jaeyoon Kim, Woo Jae Kim, Jinhwan Seo 等ICLR 2026 · 被引用 2 次
- MVInverse: Feed-forward Multiview Inverse Rendering in SecondsXiangzuo Wu, Chengwei Ren, Jun Zhou, Xiu Li 等CVPR 2026
它引用的顶会 Paper18
- 3D Gaussian Splatting for Real-Time Radiance Field RenderingBernhard Kerbl, Georgios Kopanas, Thomas Leimkühler, George DrettakisSIGGRAPH 2023 · 被引用 5,687 次
- Instant neural graphics primitives with a multiresolution hash encodingThomas Müller, Alex Evans, Christoph Schied, Alexander KellerSIGGRAPH 2022 · 被引用 4,089 次
- Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance FieldsJonathan T. Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman 等ICCV 2021 · 被引用 2,700 次
- Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance FieldsJonathan T. Barron, Ben Mildenhall, Dor Verbin, Pratul P. Srinivasan 等CVPR 2022 · 被引用 1,603 次
- Nerfies: Deformable Neural Radiance FieldsKeunhong Park, Utkarsh Sinha, Jonathan T. Barron, Sofien Bouaziz 等ICCV 2021 · 被引用 1,442 次
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