High-Fidelity SLAM Using Gaussian Splatting with Rendering-Guided
  Densification and Regularized Optimization
Shuo Sun Malcolm Mielle Achim J. Lilienthal Martin Magnusson
We propose a dense RGBD SLAM system based on 3D Gaussian Splatting that
provides metrically accurate pose tracking and visually realistic
reconstruction. To this end, we first propose a Gaussian densification strategy
based on the rendering loss to map unobserved areas and refine reobserved
areas. Second, we introduce extra regularization parameters to alleviate the
forgetting problem in the continuous mapping problem, where parameters tend to
overfit the latest frame and result in decreasing rendering quality for
previous frames. Both mapping and tracking are performed with Gaussian
parameters by minimizing re-rendering loss in a differentiable way. Compared to
recent neural and concurrently developed gaussian splatting RGBD SLAM
baselines, our method achieves state-of-the-art results on the synthetic
dataset Replica and competitive results on the real-world dataset TUM.