Dense SLAM

Dense SLAM aims to create detailed 3D models of environments while simultaneously tracking a camera's position and orientation. Current research heavily focuses on improving the accuracy and efficiency of dense mapping using various neural implicit representations, such as Gaussian splatting and neural radiance fields, often incorporating techniques like hierarchical representations and robust optimization methods to handle challenging scenarios including dynamic objects and large-scale scenes. These advancements are significant for applications like robotics, augmented reality, and 3D modeling, offering more accurate and detailed scene understanding than traditional sparse SLAM approaches.