Back to Search Start Over

Three-Dimensional Reconstruction from a Single RGB Image Using Deep Learning: A Review

Authors :
Muhammad Saif Ullah Khan
Alain Pagani
Marcus Liwicki
Didier Stricker
Muhammad Zeshan Afzal
Source :
Journal of Imaging, Vol 8, Iss 9, p 225 (2022)
Publication Year :
2022
Publisher :
MDPI AG, 2022.

Abstract

Performing 3D reconstruction from a single 2D input is a challenging problem that is trending in literature. Until recently, it was an ill-posed optimization problem, but with the advent of learning-based methods, the performance of 3D reconstruction has also significantly improved. Infinitely many different 3D objects can be projected onto the same 2D plane, which makes the reconstruction task very difficult. It is even more difficult for objects with complex deformations or no textures. This paper serves as a review of recent literature on 3D reconstruction from a single view, with a focus on deep learning methods from 2018 to 2021. Due to the lack of standard datasets or 3D shape representation methods, it is hard to compare all reviewed methods directly. However, this paper reviews different approaches for reconstructing 3D shapes as depth maps, surface normals, point clouds, and meshes; along with various loss functions and metrics used to train and evaluate these methods.

Details

Language :
English
ISSN :
2313433X
Volume :
8
Issue :
9
Database :
Directory of Open Access Journals
Journal :
Journal of Imaging
Publication Type :
Academic Journal
Accession number :
edsdoj.046d0871094063a013bfc2368d0ec4
Document Type :
article
Full Text :
https://doi.org/10.3390/jimaging8090225