  3D reconstruction is a longstanding ill-posed problem, which has been
explored for decades by the computer vision, computer graphics, and machine
learning communities. Since 2015, image-based 3D reconstruction using
convolutional neural networks (CNN) has attracted increasing interest and
demonstrated an impressive performance. Given this new era of rapid evolution,
this article provides a comprehensive survey of the recent developments in this
field. We focus on the works which use deep learning techniques to estimate the
3D shape of generic objects either from a single or multiple RGB images. We
organize the literature based on the shape representations, the network
architectures, and the training mechanisms they use. While this survey is
intended for methods which reconstruct generic objects, we also review some of
the recent works which focus on specific object classes such as human body
shapes and faces. We provide an analysis and comparison of the performance of
some key papers, summarize some of the open problems in this field, and discuss
promising directions for future research.
