# ImprovedVBGS: Real-time Continual Variational Bayes Gaussian Splatting

arXiv: https://arxiv.org/abs/2607.15542 · HTML: https://mlsanity.com/paper/2607.15542/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 13 references that are in the map.
- First posted on arXiv 17 Jul 2026.

## Builds on (most cited first, 10 of 10)

- [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)
- [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)
- [A Survey on 3D Gaussian Splatting](https://mlsanity.com/paper/2401.03890/) (2024)
- [GaussianUpdate: Continual 3D Gaussian Splatting Update for Changing Environments](https://mlsanity.com/paper/2508.08867/) (2025)
- [CL-Splats: Continual Learning of Gaussian Splatting with Local Optimization](https://mlsanity.com/paper/2506.21117/) (2025)
- [3DGEER: 3D Gaussian Rendering Made Exact and Efficient for Generic Cameras](https://mlsanity.com/paper/2505.24053/) (2025)
- [Variational Bayes Gaussian Splatting](https://mlsanity.com/paper/2410.03592/) (2024)
- [Improving Continual Learning for Gaussian Splatting based Environments Reconstruction on Commercial Off-the-Shelf Edge Devices](https://mlsanity.com/paper/2603.08499/) (2026)

## Abstract (arXiv)

On-the-fly reconstruction is a key requirement for many applications in robotics and autonomous navigation. Variational Bayes Gaussian Splatting (VBGS) enables continual learning without replay buffers using Coordinate Ascent Variational Inference (CAVI), but its per-frame iterations over all observed points make it too slow for real-time use with strict memory and latency requirements. We present ImprovedVBGS, an accelerated framework for on-the-fly continual reconstruction. This is achieved primarily through (i) spatially truncated variational inference, and (ii) improved reassignment that uses forwarding, truncation and eliminates wasteful dynamic recompilation. On the NeRF synthetic dataset, we reduce mean per-frame latency from ~84.0 s to ~0.050 s on an RTX 3070 Ti, a 1680x speed-up while maintaining reconstruction quality. We also improve novel-view synthesis quality using an exact renderer with no added training costs.