@inproceedings{f030f21b32e04cd99f2dc7629fe61f8d,
title = "FGS-SLAM: A Calibration-Free Slam Framework Using Feed-Forward 3D Gaussian Splatting",
abstract = "3D Gaussian Splatting (3DGS)-based Simultaneous Localization and Mapping (SLAM) has recently emerged as a promising paradigm for AR/VR, enabling real-time tracking and highfidelity photorealistic rendering. However, existing methods typically require accurate camera intrinsic calibration and suffer from slow 3DGS map construction, limiting immersive experiences. We present a plug-and-play monocular 3DGS-SLAM system that employs a modular feed-forward model to infer 3D Gaussian parameters from two uncalibrated views, together with a map refinement strategy tailored for feed-forward Gaussians. This is the first monocular SLAM framework leveraging a two-view 3DGS reconstruction prior, achieving real-time performance at 20 FPS.",
keywords = "3D Gaussian Splatting, 3D Reconstruction, SLAM",
author = "Xuefeng Yang and Tongtai Cao and Yue Liu",
note = "Publisher Copyright: {\textcopyright} 2026 IEEE.; 2026 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2026 ; Conference date: 21-03-2026 Through 25-03-2026",
year = "2026",
doi = "10.1109/VRW70859.2026.00298",
language = "English",
series = "Proceedings - 2026 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2026",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1247--1248",
booktitle = "Proceedings - 2026 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2026",
address = "United States",
}