MAGiC-SLAM: Multi-Agent Gaussian Globally Consistent SLAM
Vladimir Yugay, Theo Gevers, Martin R. Oswald
Abstract
Figure 1 . MAGiC-SLAM is a multi-agent SLAM method capable of novel view synthesis. Given single-camera RGBD input streams from multiple simultaneously operating agents MAGiC-SLAM estimates their trajectories and reconstructs a 3D Gaussian map that can be rendered from previously unseen viewpoints. We showcase the high-fidelity 3D Gaussian map of a real-world environment alongside multiple agent trajectories (depicted in green, yellow, and blue) within it. Our method effectively utilizes information from multiple agents to achieve centimeter-level tracking accuracy. Our mapping and map merging strategies allow for realistic rendering of color and depth, significantly improving the state of the art. Unlike previous methods, MAGiC-SLAM is flexible in the number of agents it can handle.
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.
Cited by top-tier papers5
- ODGS-SLAM: Omnidirectional Gaussian Splatting SLAMStefan Spiss, Joey Hieronimy, Marcel Ritter, Matthias HardersCVPR 2026 · 2 citations
- TopoMA: Topology-Guided Multi-Agent Dense RGB 3D Reconstruction via Distributed InferenceXuanxuan Zhang, Shuhui Shi, Tianxiang Zhang, Zhetao Guo et al.CVPR 2026 · 1 citation
- PoInit-of-View: Poisoning Initialization of Views Transfers Across Multiple 3D Reconstruction SystemsWeijie Wang, Songlong Xing, Zhengyu Zhao, Nicu Sebe et al.CVPR 2026 · 1 citation
- AERGS-SLAM: Auto-Exposure-Robust Stereo 3D Gaussian Splatting SLAMZhiyu Zhou, Feng Hui, Yu LiuCVPR 2026
- MangoBench: A Benchmark for Multi-Agent Goal-Conditioned Offline Reinforcement LearningYi Wang, Ningze Zhong, Zhiheng Fu, Longguang Wang et al.CVPR 2026
Builds on16
- 3D Gaussian Splatting for Real-Time Radiance Field RenderingBernhard Kerbl, Georgios Kopanas, Thomas Leimkühler, George DrettakisSIGGRAPH 2023 · 5,687 citations
- Instant neural graphics primitives with a multiresolution hash encodingThomas Müller, Alex Evans, Christoph Schied, Alexander KellerSIGGRAPH 2022 · 4,089 citations
- Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance FieldsJonathan T. Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman et al.ICCV 2021 · 2,700 citations
- Plenoxels: Radiance Fields without Neural NetworksSara Fridovich-Keil, Alex Yu, Matthew Tancik, Qinhong Chen et al.CVPR 2022 · 1,237 citations
- NICE-SLAM: Neural Implicit Scalable Encoding for SLAMZihan Zhu, Songyou Peng, Viktor Larsson, Weiwei Xu et al.CVPR 2022 · 720 citations
Related papers
- DG-SLAM: Robust Dynamic Gaussian Splatting SLAM with Hybrid Pose OptimizationYueming Xu, Haochen Jiang, Zhongyang Xiao, Jianfeng Feng et al.NeurIPS 2024 · 65 citations
- MNE-SLAM: Multi-Agent Neural SLAM for Mobile RobotsTianchen Deng, Guole Shen, Chen Xun, Shenghai Yuan et al.CVPR 2025
- SplaTAM: Splat, Track & Map 3D Gaussians for Dense RGB-D SLAMNikhil Varma Keetha, Jay Karhade, Krishna Murthy Jatavallabhula, Gengshan Yang et al.CVPR 2024
- WildGS-SLAM: Monocular Gaussian Splatting SLAM in Dynamic EnvironmentsJianhao Zheng, Zihan Zhu, Valentin Bieri, Marc Pollefeys et al.CVPR 2025
- RTG-SLAM: Real-time 3D Reconstruction at Scale using Gaussian SplattingZhexi Peng, Tianjia Shao, Yong Liu, Jingke Zhou et al.SIGGRAPH 2024 · 96 citations
