Deep learning

Physics

Pure mathematics

Graphics

Key (lock)

Mathematics

Cognitive neuroscience of visual object recognition

Computer graphics

3D reconstruction

Optics

Computer graphics (images)

World Wide Web

Iterative reconstruction

Machine learning

Artificial intelligence

Focus (optics)

Data science

Open research

Computer science

Object (grammar)

Field (mathematics)

Computer vision

Computer security

Convolutional neural network

IEEE Transactions on Pattern Analysis and Machine Intelligence

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.

Image-Based 3D Object Reconstruction: State-of-the-Art and Trends in the Deep Learning Era