ImprovedVBGS: Real-time Continual Variational Bayes Gaussian Splatting

ImprovedVBGS, an accelerated framework for on-the-fly continual reconstruction, reduces mean per-frame latency from ~84.0 s to ~0.050 s on an RTX 3070 Ti using the NeRF synthetic dataset, a 1680x speed-up while maintaining reconstruction quality, according to a new arXiv paper.

arXiv:2607.15542v1 Announce Type: new Abstract: 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.