GIR: 3D Gaussian Inverse Rendering for Relightable Scene Factorization
This paper presents a 3D Gaussian Inverse Rendering (GIR) method, employing 3D Gaussian representations to effectively factorize the scene into material properties, light, and geometry. The key contributions are three-fold. We compute the normal of each 3D Gaussian using the shortest eigenvector, with a directional masking scheme forcing accurate normal estimation without external supervision. We adopt an efficient voxel-based indirect illumination tracing scheme that stores direction-aware outgoing radiance in each 3D Gaussian to disentangle secondary illumination for approximating multi-bounce light transport. To further enhance the illumination disentanglement, we represent a high-resolution environmental map with a learnable low-resolution map and a lightweight, fully convolutional network. Our method achieves state-of-the-art performance in both relighting and novel view synthesis tasks among the recently proposed inverse rendering methods while achieving real-time rendering. This substantiates our proposed method's efficacy and broad applicability, highlighting its potential as an influential tool in various real-time interactive graphics applications such as material editing and relighting.
Authors
- Yahao Shi (ORCID: https://orcid.org/0009-0009-2296-0241)
- Errui Ding (ORCID: https://orcid.org/0000-0002-1867-5378)
- Bin Zhou (ORCID: https://orcid.org/0000-0002-9713-7737)
- Chenming Wu (ORCID: https://orcid.org/0000-0003-3609-2851)
- Jingdong Wang (ORCID: https://orcid.org/0000-0002-4888-4445)
- Xing Liu (ORCID: https://orcid.org/0000-0001-8122-7095)
- Jian Zhang (ORCID: https://orcid.org/0000-0002-8824-8574)
- Chen Zhao (ORCID: https://orcid.org/0000-0002-4903-0337)
- Yanmin Wu (ORCID: https://orcid.org/0000-0002-3926-4500)
- Haocheng Feng (ORCID: https://orcid.org/0000-0002-7567-3053)
Institutions
- Peking University (CN)
- Beihang University (CN)
- Baidu (China) (CN)
Publication Details
- Journal
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1109/tpami.2025.3575937
- Citations
- 9
- Primary Topic
- Advanced Vision and Imaging
- Type
- article
- Field-Weighted Citation Impact
- 11.48
Funders
- National Natural Science Foundation of China
- Beijing Science and Technology Planning Project