Articles producció científica> Enginyeria Informàtica i Matemàtiques

Monocular Depth Estimation Using Deep Learning: A Review

  • Identification data

    Identifier: imarina:9280435
    Authors:
    Masoumian, ArminRashwan, Hatem ACristiano, JulianAsif, M SalmanPuig, Domenec
    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.
  • Others:

    Author, as appears in the article.: Masoumian, Armin; Rashwan, Hatem A; Cristiano, Julian; Asif, M Salman; Puig, Domenec
    Department: Enginyeria Informàtica i Matemàtiques
    URV's Author/s: Abdellatif Fatahallah Ibrahim Mahmoud, Hatem / CRISTIANO RODRÍGUEZ, JULIÁN EFRÉN / Masoumian, Armin / Puig Valls, Domènec Savi
    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
    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.
    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
    licence for use: https://creativecommons.org/licenses/by/3.0/es/
    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
    Record's date: 2024-09-21
    Papper version: info:eu-repo/semantics/publishedVersion
    Link to the original source: https://www.mdpi.com/1424-8220/22/14/5353
    Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
    Papper original source: Sensors. 22 (14): 5353-
    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
    Article's DOI: 10.3390/s22145353
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2022
    Publication Type: Journal Publications
  • Keywords:

    Analytical Chemistry,Atomic and Molecular Physics, and Optics,Biochemistry,Chemistry, Analytical,Electrical and Electronic Engineering,Electrochemistry,Engineering, Electrical & Electronic,Information Systems,Instrumentation,Instruments & Instrumentation,Medicine (Miscellaneous)
    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
    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
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