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：3D密集重建是指从2D平面图像获取3D对象的完整形状和纹理特征的过程。 3D重建是一个重要且广泛研究的问题，但远未被解决。本文系统地介绍了基于几何和光学模型的经典3D密集重建方法，以及基于深度学习的方法。同时，介绍了深度学习数据集，以及深度学习方法在这些数据集上展示的性能和优缺点。