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
摘要
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.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
它引用的顶会 Paper26
- Segment AnythingAlexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao 等ICCV 2023 · 被引用 13,211 次
- 3D Gaussian Splatting for Real-Time Radiance Field RenderingBernhard Kerbl, Georgios Kopanas, Thomas Leimkühler, George DrettakisSIGGRAPH 2023 · 被引用 5,687 次
- Fully Convolutional Geometric FeaturesChristopher B. Choy, Jaesik Park, Vladlen KoltunICCV 2019 · 被引用 807 次
- Nerfstudio: A Modular Framework for Neural Radiance Field DevelopmentMatthew Tancik, Ethan Weber, Evonne Ng, Ruilong Li 等SIGGRAPH 2023 · 被引用 592 次
- Geometric Transformer for Fast and Robust Point Cloud RegistrationZheng Qin, Hao Yu, Changjian Wang, Yulan Guo 等CVPR 2022 · 被引用 436 次
相关 Paper
- GS-IR: 3D Gaussian Splatting for Inverse RenderingZhihao Liang, Qi Zhang, Ying Feng, Ying Shan 等CVPR 2024
- GigaGS: 3D Gaussian Based Planar Representation for Large-Scene Surface ReconstructionJunyi Chen, Weicai Ye, Yifan Wang, Danpeng Chen 等AAAI 2025 · 被引用 5 次
- UV-RGS: Relightable 3D Gaussian Splatting from Unposed Views Under Varied IlluminationsWei Feng, Chi Huang, Qi Zhang, Qian Zhang 等AAAI 2026
- SGS-Intrinsic: Semantic-Invariant Gaussian Splatting for Sparse-View Indoor Inverse RenderingJiahao Niu, Rongjia Zheng, Wenju Xu, Wei-Shi Zheng 等CVPR 2026 · 被引用 1 次
- 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
