Monocular Depth Estimation using Transfer learning-An Overview

Depth estimation is a computer vision technique that is critical for autonomous schemes for sensing their surroundings and predict their own condition. Traditional estimating approaches, such as structure from motion besides stereo vision similarity, rely on feature communications from several views...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:E3S web of conferences 2021-01, Vol.309, p.1069
Hauptverfasser: Swaraja, K., Akshitha, V., Pranav, K., Vyshnavi, B., Sai Akhil, V., Meenakshi, K., Kora, Padmavathi, Valiveti, Himabindu, Duggineni, Chaitanya
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Depth estimation is a computer vision technique that is critical for autonomous schemes for sensing their surroundings and predict their own condition. Traditional estimating approaches, such as structure from motion besides stereo vision similarity, rely on feature communications from several views to provide depth information. In the meantime, the depth maps anticipated are scarce. Gathering depth information via monocular depth estimation is an ill-posed issue, according to a substantial corpus of deep learning approaches recently suggested. Estimation of Monocular depth with deep learning has gotten a lot of interest in current years, thanks to the fast expansion of deep neural networks, and numerous strategies have been developed to solve this issue. In this study, we want to give a comprehensive assessment of the methodologies often used in the estimation of monocular depth. The purpose of this study is to look at recent advances in deep learning-based estimation of monocular depth. To begin, we'll go through the various depth estimation techniques and datasets for monocular depth estimation. A complete overview of multiple deep learning methods that use transfer learning Network designs, including several combinations of encoders and decoders, is offered. In addition, multiple deep learning-based monocular depth estimation approaches and models are classified. Finally, the use of transfer learning approaches to monocular depth estimation is illustrated.
ISSN:2267-1242
2555-0403
2267-1242
DOI:10.1051/e3sconf/202130901069