E. Tan

This work proposes a dual-covariance parameterization of 3D Gaussian Splatting, achieving robust tracking performance across multiple scenes and reduced odometry drift, while tracking at $\sim$ 60 FPS.

Achieving state-of-the-art navigation performance with only 0.58M trainable parameters challenges the notion that bigger models are always better.