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

Institutions

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

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

GIR: 3D Gaussian Inverse Rendering for Relightable Scene Factorization

Yahao Shi, Errui Ding, Bin Zhou, Chenming Wu et al.
9 citations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Advanced Vision and Imaging
11.48
article

GIR: 3D Gaussian Inverse Rendering for Relightable Scene Factorization

Yahao Shi, Errui Ding, Bin Zhou, Chenming Wu, Jingdong Wang, Xing Liu, Jian Zhang, Chen Zhao, Yanmin Wu, Haocheng Feng
article en
9 citations

Abstract

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.

IEEE Transactions on Pattern Analysis and Machine IntelligenceVol. PP
Peking University (CN), Beihang University (CN), Baidu (China) (CN)
National Natural Science Foundation of China, Beijing Science and Technology Planning Project
Openalex Percentile: Top 1%
Advanced Vision and Imaging
11.48
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.