GauUpdate: New Object Insertion in 3D Gaussian Fields with Consistent Global Illumination
Chengwei Ren, Fan Zhang, Liangchao Xu, Liang Pan, Ziwei Liu, Wenping Wang, Xiao-Ping Zhang, Yuan Liu
Abstract
3D Gaussian Splatting (3DGS) is a prevailing technique to reconstruct large-scale 3D scenes from multiview images for novel view synthesis, like a room, a block, and even a city. Such large-scale scenes are not static with changes constantly happening in these scenes, like a new building being built or a new decoration being set up. To keep the reconstructed 3D Gaussian fields up-to-date, a naive way is to reconstruct the whole scene after changing, which is extremely costly and inefficient. In this paper, we propose a new method called GauUpdate that allows partially updating an old 3D Gaussian field with new objects from a new 3D Gaussian field. However, simply inserting the new objects leads to inconsistent appearances because the old and new Gaussian fields may have different lighting environments from each other. GauUpdate addresses this problem by applying inverse rendering techniques in the 3DGS to recover both the materials and environmental lights. Based on the materials and lighting, we relight the new objects in the old 3D Gaussian field for consistent global illumination. For an accurate estimation of the materials and lighting, we put additional constraints on the materials and lighting conditions, that these two fields share the same materials but different environment lights, to improve their qualities. We conduct experiments on both synthetic scenes and real-world scenes to evaluate GauUpdate, which demonstrate that GauUpdate achieves realistic object insertion in 3D Gaussian fields with consistent appearances.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext e29aaa8b-28f0-4b34-ad27-1fc246f6d531Builds on26
- Segment AnythingAlexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao et al.ICCV 2023 · 13,211 citations
- 3D Gaussian Splatting for Real-Time Radiance Field RenderingBernhard Kerbl, Georgios Kopanas, Thomas Leimkühler, George DrettakisSIGGRAPH 2023 · 5,687 citations
- Fully Convolutional Geometric FeaturesChristopher B. Choy, Jaesik Park, Vladlen KoltunICCV 2019 · 807 citations
- Nerfstudio: A Modular Framework for Neural Radiance Field DevelopmentMatthew Tancik, Ethan Weber, Evonne Ng, Ruilong Li et al.SIGGRAPH 2023 · 592 citations
- Geometric Transformer for Fast and Robust Point Cloud RegistrationZheng Qin, Hao Yu, Changjian Wang, Yulan Guo et al.CVPR 2022 · 436 citations
Related papers
- GS-IR: 3D Gaussian Splatting for Inverse RenderingZhihao Liang, Qi Zhang, Ying Feng, Ying Shan et al.CVPR 2024
- GigaGS: 3D Gaussian Based Planar Representation for Large-Scene Surface ReconstructionJunyi Chen, Weicai Ye, Yifan Wang, Danpeng Chen et al.AAAI 2025 · 5 citations
- UV-RGS: Relightable 3D Gaussian Splatting from Unposed Views Under Varied IlluminationsWei Feng, Chi Huang, Qi Zhang, Qian Zhang et al.AAAI 2026
- SGS-Intrinsic: Semantic-Invariant Gaussian Splatting for Sparse-View Indoor Inverse RenderingJiahao Niu, Rongjia Zheng, Wenju Xu, Wei-Shi Zheng et al.CVPR 2026 · 1 citation
- RTR-GS: 3D Gaussian Splatting for Inverse Rendering with Radiance Transfer and ReflectionYongyang Zhou, Fanglue Zhang, Zichen Wang, Lei ZhangACM MM 2025 · 4 citations
