  Dense simultaneous localization and mapping (SLAM) is crucial for robotics
and augmented reality applications. However, current methods are often hampered
by the non-volumetric or implicit way they represent a scene. This work
introduces SplaTAM, an approach that, for the first time, leverages explicit
volumetric representations, i.e., 3D Gaussians, to enable high-fidelity
reconstruction from a single unposed RGB-D camera, surpassing the capabilities
of existing methods. SplaTAM employs a simple online tracking and mapping
system tailored to the underlying Gaussian representation. It utilizes a
silhouette mask to elegantly capture the presence of scene density. This
combination enables several benefits over prior representations, including fast
rendering and dense optimization, quickly determining if areas have been
previously mapped, and structured map expansion by adding more Gaussians.
Extensive experiments show that SplaTAM achieves up to 2x superior performance
in camera pose estimation, map construction, and novel-view synthesis over
existing methods, paving the way for more immersive high-fidelity SLAM
applications.
