# Variational Bayes Gaussian Splatting

arXiv: https://arxiv.org/abs/2410.03592 · HTML: https://mlsanity.com/paper/2410.03592/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 28 references that are in the map.
- First posted on arXiv 4 Oct 2024.

## Builds on (most cited first, 15 of 15)

- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [Variational Inference: A Review for Statisticians](https://mlsanity.com/paper/1601.00670/) (2016)
- [Depth Anything V2](https://mlsanity.com/paper/2406.09414/) (2024)
- [Habitat: A Platform for Embodied AI Research](https://mlsanity.com/paper/1904.01201/) (2019)
- [The Replica Dataset: A Digital Replica of Indoor Spaces](https://mlsanity.com/paper/1906.05797/) (2019)
- [A Comprehensive Survey of Continual Learning: Theory, Method and Application](https://mlsanity.com/paper/2302.00487/) (2023)
- [Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understanding](https://mlsanity.com/paper/2011.02523/) (2020)
- [iMAP: Implicit Mapping and Positioning in Real-Time](https://mlsanity.com/paper/2103.12352/) (2021)
- [SplaTAM: Splat, Track & Map 3D Gaussians for Dense RGB-D SLAM](https://mlsanity.com/paper/2312.02126/) (2023)
- [A Survey on 3D Gaussian Splatting](https://mlsanity.com/paper/2401.03890/) (2024)
- [3D Gaussian Splatting as Markov Chain Monte Carlo](https://mlsanity.com/paper/2404.09591/) (2024)
- [COLMAP-Free 3D Gaussian Splatting](https://mlsanity.com/paper/2312.07504/) (2023)
- [A Survey of Structure from Motion](https://mlsanity.com/paper/1701.08493/) (2017)
- [3D Gaussian as a New Era: A Survey](https://mlsanity.com/paper/2402.07181/) (2024)
- [CLNeRF: Continual Learning Meets NeRF](https://mlsanity.com/paper/2308.14816/) (2023)

## Built on by (most cited first, 7 of 7)

- [GP-4DGS: Probabilistic 4D Gaussian Splatting from Monocular Video via Variational Gaussian Processes](https://mlsanity.com/paper/2604.02915/) (2026)
- [VBGS-SLAM: Variational Bayesian Gaussian Splatting Simultaneous Localization and Mapping](https://mlsanity.com/paper/2604.02696/) (2026)
- [When Variance Is Not an Error Map: Calibrated Uncertainty for Radiative Gaussian Splatting in Sparse-View CT](https://mlsanity.com/paper/2607.13682/) (2026)
- [λ-VAE: Variance Equalization for Posterior Collapse](https://mlsanity.com/paper/2607.05531/) (2026)
- [DP-Splat: Bayesian Nonparametric Complexity Control for Gaussian Splatting](https://mlsanity.com/paper/2607.10912/) (2026)
- [ImprovedVBGS: Real-time Continual Variational Bayes Gaussian Splatting](https://mlsanity.com/paper/2607.15542/) (2026)
- [PUGS: Physics-Guided Uncertainty Modeling for Underwater Dynamic 3D Gaussian Splatting](https://mlsanity.com/paper/2610.17936/) (2026)

## Abstract (arXiv)

Recently, 3D Gaussian Splatting has emerged as a promising approach for modeling 3D scenes using mixtures of Gaussians. The predominant optimization method for these models relies on backpropagating gradients through a differentiable rendering pipeline, which struggles with catastrophic forgetting when dealing with continuous streams of data. To address this limitation, we propose Variational Bayes Gaussian Splatting (VBGS), a novel approach that frames training a Gaussian splat as variational inference over model parameters. By leveraging the conjugacy properties of multivariate Gaussians, we derive a closed-form variational update rule, allowing efficient updates from partial, sequential observations without the need for replay buffers. Our experiments show that VBGS not only matches state-of-the-art performance on static datasets, but also enables continual learning from sequentially streamed 2D and 3D data, drastically improving performance in this setting.