Depth Completion Model

Depth completion models aim to reconstruct full, dense depth maps from sparse or incomplete depth measurements, often incorporating information from RGB images or other sensors like LiDAR. Current research focuses on improving accuracy and efficiency through various approaches, including convolutional neural networks (CNNs), transformers, and hybrid architectures that combine both, as well as leveraging self-supervised learning and incorporating semantic segmentation information to guide the completion process. These advancements are crucial for applications in robotics, autonomous driving, and 3D scene understanding, enabling more robust and reliable perception in challenging environments. The development of efficient and accurate depth completion is driving progress in several fields that rely on accurate 3D scene reconstruction.