Dense Depth Estimation

Dense depth estimation aims to reconstruct detailed 3D scene geometry from various input modalities, including images, LiDAR, radar, and inertial measurements. Current research focuses on improving accuracy and robustness through sensor fusion (e.g., combining radar and cameras, stereo and LiDAR), innovative network architectures like transformers and deformable convolutions, and the incorporation of geometric priors to guide depth completion. These advancements are crucial for applications such as autonomous driving, robotics, and 3D scene reconstruction, enabling more reliable and accurate perception in challenging environments.