Object reconstruction is one of the most crucial
branches of computer vision. With the development of deep
learning, many tasks have achieved remarkable improvements
in computer vision. 3D reconstruction with deep learning also
has attracted much attention in recent years. Deep learning
methods based on CNN-based and GAN-based architectures
have been adopted for 3D object prediction. In addition,
researchers utilize different inputs such as RGB and depth
images to achieve prediction based on different problem. In
this paper, we provide a detailed overview of recent advances
in 3D object reconstruction. The reviewed approaches are
categorized into three groups depending on the input modality:
RGB-based, depth-based and other-input-based. Particularly,
we introduce the various methods and indirectly classify the
shape representation. As a survey, we discuss the strong and
weak points of exciting approaches.