Online Dense Mapping

Online dense mapping aims to create detailed 3D models of environments in real-time using sensor data, primarily focusing on efficient and accurate reconstruction. Current research emphasizes improving the robustness and speed of algorithms, often employing implicit neural representations like 3D Gaussian splatting and variations of neural radiance fields (NeRFs), along with optimized data structures like octrees and hybrid representations to handle large-scale scenes and mitigate memory limitations. These advancements are crucial for applications in robotics, autonomous driving, augmented reality, and the creation of digital twins, enabling more sophisticated interaction with and understanding of the physical world.