Deep Learning-based Depth Estimation Methods from Monocular Image and Videos: A Comprehensive Survey-Reference-Cited by-同舟云学术

Deep Learning-based Depth Estimation Methods from Monocular Image and Videos: A Comprehensive Survey

Published:2024-07-15 Issue: Volume: Page:
ISSN:0360-0300
Container-title:ACM Computing Surveys
language:en
Short-container-title:ACM Comput. Surv.

Author:

Rajapaksha Uchitha¹^ORCID,Sohel Ferdous¹^ORCID,LAGA HAMID¹^ORCID,Diepeveen Dean²³^ORCID,Bennamoun Mohammed⁴^ORCID

Affiliation:

1. School of Information Technology, Murdoch University, Murdoch, Australia

2. Murdoch University, Murdoch, Australia

3. Western Australia Department of Primary Industries and Regional Development, South Perth, Australia

4. Department of Computer Science and Software Engineering, The University of Western Australia, Perth Australia

Abstract

Estimating depth from single RGB images and videos is of widespread interest due to its applications in many areas, including autonomous driving, 3D reconstruction, digital entertainment, and robotics. More than 500 deep learning-based papers have been published in the past 10 years, which indicates the growing interest in the task. This paper presents a comprehensive survey of the existing deep learning-based methods, the challenges they address, and how they have evolved in their architecture and supervision methods. It provides a taxonomy for classifying the current work based on their input and output modalities, network architectures, and learning methods. It also discusses the major milestones in the history of monocular depth estimation, and different pipelines, datasets, and evaluation metrics used in existing methods.

Publisher

Association for Computing Machinery (ACM)

Link

https://dl.acm.org/doi/pdf/10.1145/3677327

Reference213 articles.

1. Large-Scale Data for Multiple-View Stereopsis

2. Filippo Aleotti, Fabio Tosi, Matteo Poggi, and Stefano Mattoccia. 2018. Generative adversarial networks for unsupervised monocular depth prediction. In Proceedings of the European conference on computer vision workshops. 0–0.

3. Semi-Supervised Monocular Depth Estimation with Left-Right Consistency Using Deep Neural Network

4. Amir Atapour-Abarghouei and Toby P Breckon. 2018. Real-time monocular depth estimation using synthetic data with domain adaptation via image style transfer. In IEEE conference on computer vision and pattern recognition. 2800–2810.

5. Dylan Auty and Krystian Mikolajczyk. 2023. Learning to prompt clip for monocular depth estimation: Exploring the limits of human language. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 2039–2047.