  3D dense reconstruction refers to the process of obtaining the complete shape
and texture features of 3D objects from 2D planar images. 3D reconstruction is
an important and extensively studied problem, but it is far from being solved.
This work systematically introduces classical methods of 3D dense
reconstruction based on geometric and optical models, as well as methods based
on deep learning. It also introduces datasets for deep learning and the
performance and advantages and disadvantages demonstrated by deep learning
methods on these datasets.
