Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

3D Reconstruction from a Single RGB Image using Deep Learning: A Review

Muhammad Saif Ullah Khan
* ORCID logo ,
Alain Pagani
,
Marcus Liwicki
ORCID logo ,
Didier Stricker
,
Muhammad Zeshan Afzal
*
Version 1 : Received: 1 August 2022 / Approved: 2 August 2022 / Online: 2 August 2022 (12:17:08 CEST)

A peer-reviewed article of this Preprint also exists.

Khan, M.S.U.; Pagani, A.; Liwicki, M.; Stricker, D.; Afzal, M.Z. Three-Dimensional Reconstruction from a Single RGB Image Using Deep Learning: A Review. J. Imaging 2022, 8, 225. Khan, M.S.U.; Pagani, A.; Liwicki, M.; Stricker, D.; Afzal, M.Z. Three-Dimensional Reconstruction from a Single RGB Image Using Deep Learning: A Review. J. Imaging 2022, 8, 225.

Abstract

3D reconstruction from a single 2D input is a classic problem in the field of computer vision. With the advancements in deep learning, the performance of 3D reconstruction has also significantly improved. The reconstruction task is more difficult for objects with no textures or complex deformations. 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 lack of standard datasets or 3D shape representation methods, it is hard make direct comparisons between all reviewed methods. However, this paper reviews different approaches for reconstructing 3d shape as depth maps, surface normals, point clouds and meshes; along with various loss functions and evaluation metrics used to train and evaluate these methods.

Keywords

deep learning; 3D reconstruction; convolutional neural networks; texture-less surfaces

Subject

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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