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.

翻译：三维稠密重建是指从二维平面图像中获取三维物体的完整形状与纹理特征的过程。三维重建是一个重要且被广泛研究的问题，但远未得到解决。本文系统介绍了基于几何与光学模型的经典三维稠密重建方法，以及基于深度学习的方法。同时介绍了深度学习所依赖的数据集，以及深度学习方法在这些数据集上展现的性能表现与优劣特征。