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Joint Soft–Hard Attention for Self-Supervised Monocular Depth Estimation
In recent years, self-supervised monocular depth estimation has gained popularity among researchers because it uses only a single camera at a much lower cost than the direct use of laser sensors to acquire depth. Although monocular self-supervised methods can obtain dense depths, the estimation accu...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8588100/ https://www.ncbi.nlm.nih.gov/pubmed/34770263 http://dx.doi.org/10.3390/s21216956 |
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author | Fan, Chao Yin, Zhenyu Xu, Fulong Chai, Anying Zhang, Feiqing |
author_facet | Fan, Chao Yin, Zhenyu Xu, Fulong Chai, Anying Zhang, Feiqing |
author_sort | Fan, Chao |
collection | PubMed |
description | In recent years, self-supervised monocular depth estimation has gained popularity among researchers because it uses only a single camera at a much lower cost than the direct use of laser sensors to acquire depth. Although monocular self-supervised methods can obtain dense depths, the estimation accuracy needs to be further improved for better applications in scenarios such as autonomous driving and robot perception. In this paper, we innovatively combine soft attention and hard attention with two new ideas to improve self-supervised monocular depth estimation: (1) a soft attention module and (2) a hard attention strategy. We integrate the soft attention module in the model architecture to enhance feature extraction in both spatial and channel dimensions, adding only a small number of parameters. Unlike traditional fusion approaches, we use the hard attention strategy to enhance the fusion of generated multi-scale depth predictions. Further experiments demonstrate that our method can achieve the best self-supervised performance both on the standard KITTI benchmark and the Make3D dataset. |
format | Online Article Text |
id | pubmed-8588100 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-85881002021-11-13 Joint Soft–Hard Attention for Self-Supervised Monocular Depth Estimation Fan, Chao Yin, Zhenyu Xu, Fulong Chai, Anying Zhang, Feiqing Sensors (Basel) Article In recent years, self-supervised monocular depth estimation has gained popularity among researchers because it uses only a single camera at a much lower cost than the direct use of laser sensors to acquire depth. Although monocular self-supervised methods can obtain dense depths, the estimation accuracy needs to be further improved for better applications in scenarios such as autonomous driving and robot perception. In this paper, we innovatively combine soft attention and hard attention with two new ideas to improve self-supervised monocular depth estimation: (1) a soft attention module and (2) a hard attention strategy. We integrate the soft attention module in the model architecture to enhance feature extraction in both spatial and channel dimensions, adding only a small number of parameters. Unlike traditional fusion approaches, we use the hard attention strategy to enhance the fusion of generated multi-scale depth predictions. Further experiments demonstrate that our method can achieve the best self-supervised performance both on the standard KITTI benchmark and the Make3D dataset. MDPI 2021-10-20 /pmc/articles/PMC8588100/ /pubmed/34770263 http://dx.doi.org/10.3390/s21216956 Text en © 2021 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 | Article Fan, Chao Yin, Zhenyu Xu, Fulong Chai, Anying Zhang, Feiqing Joint Soft–Hard Attention for Self-Supervised Monocular Depth Estimation |
title | Joint Soft–Hard Attention for Self-Supervised Monocular Depth Estimation |
title_full | Joint Soft–Hard Attention for Self-Supervised Monocular Depth Estimation |
title_fullStr | Joint Soft–Hard Attention for Self-Supervised Monocular Depth Estimation |
title_full_unstemmed | Joint Soft–Hard Attention for Self-Supervised Monocular Depth Estimation |
title_short | Joint Soft–Hard Attention for Self-Supervised Monocular Depth Estimation |
title_sort | joint soft–hard attention for self-supervised monocular depth estimation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8588100/ https://www.ncbi.nlm.nih.gov/pubmed/34770263 http://dx.doi.org/10.3390/s21216956 |
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