Dense 3D

Dense 3D reconstruction aims to create detailed, three-dimensional models of scenes from various input data, such as images, LiDAR scans, and event camera streams. Current research focuses on improving the efficiency and accuracy of these reconstructions, employing techniques like neural implicit representations (NeRFs), 3D Gaussian splatting, and voxel-based methods, often integrated with simultaneous localization and mapping (SLAM) for dynamic environments. These advancements are crucial for applications ranging from autonomous driving and robotics to augmented reality and cultural heritage preservation, enabling more accurate and robust scene understanding in real-time.