Real Time Dense 3D Reconstruction

Real-time dense 3D reconstruction aims to create detailed three-dimensional models of scenes from video or image sequences in real-time, enabling immediate applications in robotics and augmented reality. Current research focuses on improving efficiency and accuracy using techniques like 3D Gaussian splatting, which represents scenes as collections of 3D Gaussian distributions, and integrating neural implicit representations for more robust and detailed scene modeling. These advancements leverage both monocular and multi-view approaches, often incorporating deep learning for depth estimation and scene consistency, and are driving progress in applications requiring immediate environmental understanding.