Neural Implicit SLAM

Neural Implicit SLAM integrates neural implicit representations, like Neural Radiance Fields (NeRFs), with Simultaneous Localization and Mapping (SLAM) to create 3D scene reconstructions from sensor data. Current research focuses on improving efficiency through sparse representations (e.g., tri-plane encoding, point-based methods), enhancing robustness to dynamic environments and sensor noise (e.g., incorporating semantic features, event cameras), and achieving global map consistency. This approach offers advantages in rendering quality, scene understanding, and compact map representation, impacting robotics, augmented reality, and 3D modeling applications.