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A Critical Review of Deep Learning-Based Multi-Sensor Fusion Techniques
In this review, we provide a detailed coverage of multi-sensor fusion techniques that use RGB stereo images and a sparse LiDAR-projected depth map as input data to output a dense depth map prediction. We cover state-of-the-art fusion techniques which, in recent years, have been deep learning-based m...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9737943/ https://www.ncbi.nlm.nih.gov/pubmed/36502065 http://dx.doi.org/10.3390/s22239364 |
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author | Marsh, Benedict Sadka, Abdul Hamid Bahai, Hamid |
author_facet | Marsh, Benedict Sadka, Abdul Hamid Bahai, Hamid |
author_sort | Marsh, Benedict |
collection | PubMed |
description | In this review, we provide a detailed coverage of multi-sensor fusion techniques that use RGB stereo images and a sparse LiDAR-projected depth map as input data to output a dense depth map prediction. We cover state-of-the-art fusion techniques which, in recent years, have been deep learning-based methods that are end-to-end trainable. We then conduct a comparative evaluation of the state-of-the-art techniques and provide a detailed analysis of their strengths and limitations as well as the applications they are best suited for. |
format | Online Article Text |
id | pubmed-9737943 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-97379432022-12-11 A Critical Review of Deep Learning-Based Multi-Sensor Fusion Techniques Marsh, Benedict Sadka, Abdul Hamid Bahai, Hamid Sensors (Basel) Review In this review, we provide a detailed coverage of multi-sensor fusion techniques that use RGB stereo images and a sparse LiDAR-projected depth map as input data to output a dense depth map prediction. We cover state-of-the-art fusion techniques which, in recent years, have been deep learning-based methods that are end-to-end trainable. We then conduct a comparative evaluation of the state-of-the-art techniques and provide a detailed analysis of their strengths and limitations as well as the applications they are best suited for. MDPI 2022-12-01 /pmc/articles/PMC9737943/ /pubmed/36502065 http://dx.doi.org/10.3390/s22239364 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Marsh, Benedict Sadka, Abdul Hamid Bahai, Hamid A Critical Review of Deep Learning-Based Multi-Sensor Fusion Techniques |
title | A Critical Review of Deep Learning-Based Multi-Sensor Fusion Techniques |
title_full | A Critical Review of Deep Learning-Based Multi-Sensor Fusion Techniques |
title_fullStr | A Critical Review of Deep Learning-Based Multi-Sensor Fusion Techniques |
title_full_unstemmed | A Critical Review of Deep Learning-Based Multi-Sensor Fusion Techniques |
title_short | A Critical Review of Deep Learning-Based Multi-Sensor Fusion Techniques |
title_sort | critical review of deep learning-based multi-sensor fusion techniques |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9737943/ https://www.ncbi.nlm.nih.gov/pubmed/36502065 http://dx.doi.org/10.3390/s22239364 |
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