  We present a dense simultaneous localization and mapping (SLAM) method that
uses 3D Gaussians as a scene representation. Our approach enables
interactive-time reconstruction and photo-realistic rendering from real-world
single-camera RGBD videos. To this end, we propose a novel effective strategy
for seeding new Gaussians for newly explored areas and their effective online
optimization that is independent of the scene size and thus scalable to larger
scenes. This is achieved by organizing the scene into sub-maps which are
independently optimized and do not need to be kept in memory. We further
accomplish frame-to-model camera tracking by minimizing photometric and
geometric losses between the input and rendered frames. The Gaussian
representation allows for high-quality photo-realistic real-time rendering of
real-world scenes. Evaluation on synthetic and real-world datasets demonstrates
competitive or superior performance in mapping, tracking, and rendering
compared to existing neural dense SLAM methods.
