# VBGS-SLAM: Variational Bayesian Gaussian Splatting Simultaneous Localization and Mapping

arXiv: https://arxiv.org/abs/2604.02696 · HTML: https://mlsanity.com/paper/2604.02696/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 42 references that are in the map.
- First posted on arXiv 3 Apr 2026.

## Builds on (most cited first, 26 of 26)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation](https://mlsanity.com/paper/1901.05103/) (2019)
- [Occupancy Networks: Learning 3D Reconstruction in Function Space](https://mlsanity.com/paper/1812.03828/) (2018)
- [Variational Inference: A Review for Statisticians](https://mlsanity.com/paper/1601.00670/) (2016)
- [ORB-SLAM: a Versatile and Accurate Monocular SLAM System](https://mlsanity.com/paper/1502.00956/) (2015)
- [The Replica Dataset: A Digital Replica of Indoor Spaces](https://mlsanity.com/paper/1906.05797/) (2019)
- [ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras](https://mlsanity.com/paper/1610.06475/) (2016)
- [NICE-SLAM: Neural Implicit Scalable Encoding for SLAM](https://mlsanity.com/paper/2112.12130/) (2021)
- [Gaussian Splatting SLAM](https://mlsanity.com/paper/2312.06741/) (2023)
- [SplaTAM: Splat, Track & Map 3D Gaussians for Dense RGB-D SLAM](https://mlsanity.com/paper/2312.02126/) (2023)
- [MASt3R-SLAM: Real-Time Dense SLAM with 3D Reconstruction Priors](https://mlsanity.com/paper/2412.12392/) (2024)
- [Point-SLAM: Dense Neural Point Cloud-based SLAM](https://mlsanity.com/paper/2304.04278/) (2023)
- [Vox-Fusion: Dense Tracking and Mapping with Voxel-based Neural Implicit Representation](https://mlsanity.com/paper/2210.15858/) (2022)
- [DI-Fusion: Online Implicit 3D Reconstruction with Deep Priors](https://mlsanity.com/paper/2012.05551/) (2020)
- [DGS-SLAM: Gaussian Splatting SLAM in Dynamic Environment](https://mlsanity.com/paper/2411.10722/) (2024)
- [DynaGSLAM: Real-Time Gaussian-Splatting SLAM for Online Rendering, Tracking, Motion Predictions of Moving Objects in Dynamic Scenes](https://mlsanity.com/paper/2503.11979/) (2025)
- [GS-LIVO: Real-Time LiDAR, Inertial, and Visual Multi-sensor Fused Odometry with Gaussian Mapping](https://mlsanity.com/paper/2501.08672/) (2025)
- [NeRF-VINS: A Real-time Neural Radiance Field Map-based Visual-Inertial Navigation System](https://mlsanity.com/paper/2309.09295/) (2023)
- [Variational Bayes Gaussian Splatting](https://mlsanity.com/paper/2410.03592/) (2024)
- [Large-Scale Gaussian Splatting SLAM](https://mlsanity.com/paper/2505.09915/) (2025)
- [PL-CVIO: Point-Line Cooperative Visual-Inertial Odometry](https://mlsanity.com/paper/2311.05717/) (2023)
- [Visual Navigation Using Sparse Optical Flow and Time-to-Transit](https://mlsanity.com/paper/2111.09669/) (2021)
- [NeRF-VIO: Map-Based Visual-Inertial Odometry with Initialization Leveraging Neural Radiance Fields](https://mlsanity.com/paper/2503.07952/) (2025)
- [PLK-Calib: Single-shot and Target-less LiDAR-Camera Extrinsic Calibration using Pl"ucker Lines](https://mlsanity.com/paper/2503.07955/) (2025)
- [Intelligent Hotel ROS-based Service Robot](https://mlsanity.com/paper/2009.00594/) (2020)

## Built on by (most cited first, 1 of 1)

- [DP-Splat: Bayesian Nonparametric Complexity Control for Gaussian Splatting](https://mlsanity.com/paper/2607.10912/) (2026)

## Abstract (arXiv)

3D Gaussian Splatting (3DGS) has shown promising results for 3D scene modeling using mixtures of Gaussians, yet its existing simultaneous localization and mapping (SLAM) variants typically rely on direct, deterministic pose optimization against the splat map, making them sensitive to initialization and susceptible to catastrophic forgetting as map evolves. We propose Variational Bayesian Gaussian Splatting SLAM (VBGS-SLAM), a novel framework that couples the splat map refinement and camera pose tracking in a generative probabilistic form. By leveraging conjugate properties of multivariate Gaussians and variational inference, our method admits efficient closed-form updates and explicitly maintains posterior uncertainty over both poses and scene parameters. This uncertainty-aware method mitigates drift and enhances robustness in challenging conditions, while preserving the efficiency and rendering quality of existing 3DGS. Our experiments demonstrate superior tracking performance and robustness in long sequence prediction, alongside efficient, high-quality novel view synthesis across diverse synthetic and real-world scenes.