GlobalDepth: Global-Aware Attention Model for Unsupervised Monocular Depth Estimation

Huimin Yu, Ruoqi Li, Zhuoling Xiao*, Bo Yan

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Monocular depth estimation is a significant task in computer vision, which can be widely used in Simultaneous Localization and Mapping (SLAM) and navigation. However, the current unsupervised approaches have limitations in global information perception, especially at distant objects and the boundaries of the objects. To overcome this weakness, we propose a global-aware attention model called GlobalDepth for depth estimation, which includes two essential modules: Global Feature Extraction (GFE) and Selective Feature Fusion (SFF). GFE considers the correlation among multiple channels and refines the encoder feature by extending the receptive field of the network. Furthermore, we restructure the skip connection by employing SFF between the low-level and the high-level features in element wise, rather than simply concatenation or addition at the feature level. Our model excavates the key information and enhances the ability of global perception to predict details of the scene. Extensive experimental results demonstrate that our method reduces the absolute relative error by 10.32% compared with other state-of-the-art models on KITTI datasets.

Original languageEnglish
Title of host publicationISCAS 2023 - 56th IEEE International Symposium on Circuits and Systems, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665451093
DOIs
Publication statusPublished - 2023
Externally publishedYes
Event56th IEEE International Symposium on Circuits and Systems, ISCAS 2023 - Monterey, United States
Duration: 21 May 202325 May 2023

Publication series

NameProceedings - IEEE International Symposium on Circuits and Systems
Volume2023-May
ISSN (Print)0271-4310

Conference

Conference56th IEEE International Symposium on Circuits and Systems, ISCAS 2023
Country/TerritoryUnited States
CityMonterey
Period21/05/2325/05/23

Keywords

  • global feature extraction
  • selective feature fusion
  • unsupervised monocular depth estiamtion

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