Repositori institucional URV
| URV's Author/s: | Abdellatif Fatahallah Ibrahim Mahmoud, Hatem / CRISTIANO RODRÍGUEZ, JULIÁN EFRÉN / Masoumian, Armin / Puig Valls, Domènec Savi |
| Author, as appears in the article.: | Masoumian, Armin; Rashwan, Hatem A; Cristiano, Julian; Asif, M Salman; Puig, Domenec |
| Author's mail: | hatem.abdellatif@urv.cat armin.masoumian@estudiants.urv.cat armin.masoumian@estudiants.urv.cat domenec.puig@urv.cat |
| Author identifier: | 0000-0001-5421-1637 0000-0002-0562-4205 |
| Journal publication year: | 2022 |
| Publication Type: | Journal Publications |
| APA: | Masoumian, Armin; Rashwan, Hatem A; Cristiano, Julian; Asif, M Salman; Puig, Domenec (2022). Monocular Depth Estimation Using Deep Learning: A Review. Sensors, 22(14), 5353-. DOI: 10.3390/s22145353 |
| Papper original source: | Sensors. 22 (14): 5353- |
| Abstract: | In current decades, significant advancements in robotics engineering and autonomous vehicles have improved the requirement for precise depth measurements. Depth estimation (DE) is a traditional task in computer vision that can be appropriately predicted by applying numerous procedures. This task is vital in disparate applications such as augmented reality and target tracking. Conventional monocular DE (MDE) procedures are based on depth cues for depth prediction. Various deep learning techniques have demonstrated their potential applications in managing and supporting the traditional ill-posed problem. The principal purpose of this paper is to represent a state-of-the-art review of the current developments in MDE based on deep learning techniques. For this goal, this paper tries to highlight the critical points of the state-of-the-art works on MDE from disparate aspects. These aspects include input data shapes and training manners such as supervised, semi-supervised, and unsupervised learning approaches in combination with applying different datasets and evaluation indicators. At last, limitations regarding the accuracy of the DL-based MDE models, computational time requirements, real-time inference, transferability, input images shape and domain adaptation, and generalization are discussed to open new directions for future research. |
| Article's DOI: | 10.3390/s22145353 |
| Link to the original source: | https://www.mdpi.com/1424-8220/22/14/5353 |
| Papper version: | info:eu-repo/semantics/publishedVersion |
| licence for use: | https://creativecommons.org/licenses/by/3.0/es/ |
| Department: | Enginyeria Informàtica i Matemàtiques |
| Licence document URL: | https://repositori.urv.cat/ca/proteccio-de-dades/ |
| Thematic Areas: | Zootecnia / recursos pesqueiros Química Medicine (miscellaneous) Medicina veterinaria Medicina iii Medicina ii Medicina i Materiais Matemática / probabilidade e estatística Linguística e literatura Letras / linguística Interdisciplinar Instruments & instrumentation Instrumentation Information systems Geografía Geociências Farmacia Engineering, electrical & electronic Engenharias iv Engenharias iii Engenharias ii Engenharias i Electrochemistry Electrical and electronic engineering Educação física Ciências biológicas iii Ciências biológicas ii Ciências biológicas i Ciências ambientais Ciências agrárias i Ciência de alimentos Ciência da computação Chemistry, analytical Biotecnología Biodiversidade Biochemistry Atomic and molecular physics, and optics Astronomia / física Arquitetura, urbanismo e design Analytical chemistry |
| Keywords: | Supervised, semi-supervised, and unsupervised learning Supervised Single image depth estimation Semi-supervised Multi-task learning Monocular depth estimation Forecasting Deep learning Camera Augmented reality And unsupervised learning |
| Entity: | Universitat Rovira i Virgili |
| Record's date: | 2024-09-21 |
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