Journal article
Image-Based 3D object reconstruction: State-of-the-Art and trends in the deep learning era
IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.43(5), pp.1578-1604
2021
Abstract
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.
Details
- Title
- Image-Based 3D object reconstruction: State-of-the-Art and trends in the deep learning era
- Authors/Creators
- X-F Han (Author/Creator) - Southwest UniversityH. Laga (Author/Creator) - Murdoch UniversityM. Bennamoun (Author/Creator) - The University of Western Australia
- Publication Details
- IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.43(5), pp.1578-1604
- Publisher
- IEEE
- Identifiers
- 991005542649107891
- Copyright
- © 2021 IEEE
- Murdoch Affiliation
- Information Technology, Mathematics and Statistics
- Language
- English
- Resource Type
- Journal article
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Highly Cited Paper
- Collaboration types
- Domestic collaboration
- International collaboration
- Citation topics
- 4 Electrical Engineering, Electronics & Computer Science
- 4.17 Computer Vision & Graphics
- 4.17.2798 Stereo Depth Estimation
- Web Of Science research areas
- Computer Science, Artificial Intelligence
- Engineering, Electrical & Electronic
- ESI research areas
- Engineering