With the rapid advancement of deep learning technology, 3D reconstruction has achieved significant breakthroughs, becoming a pivotal research direction in computer vision. This paper reviews the applications and recent advancements of deep learning in 3D reconstruction and provides an in-depth discussion of various techniques and emerging trends in the field. Deep learning enables the automatic extraction of features from images through the training of large-scale datasets, facilitating precise reconstruction of 3D shapes. The paper explores 3D reconstruction approaches based on deep learning, focusing on two primary representation types: explicit and implicit. In addition, it introduces classical 3D reconstruction datasets, which provide valuable data resources for training and validating deep learning models. Finally, the paper concludes with a summary and an outlook on future directions in 3D reconstruction research.