Exploring the vulnerability of self-supervised monocular depth estimation models-Reference-Cited by-同舟云学术

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Exploring the vulnerability of self-supervised monocular depth estimation models

Published:2024-08 Issue: Volume:677 Page:120874
ISSN:0020-0255
Container-title:Information Sciences
language:en
Short-container-title:Information Sciences

Author:

Hou Ruitao^ORCID,Mo Kanghua^ORCID,Long Yucheng,Li Ning,Rao Yuan^ORCID

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Reference35 articles.

1. Monocular depth estimation: a thorough review;Arampatzakis;IEEE Trans. Pattern Anal. Mach. Intell.,2023

2. Physical attack on monocular depth estimation with optimal adversarial patches;Cheng,2022

3. Lira: learnable, imperceptible and robust backdoor attacks;Doan,2021

4. Towards real-time monocular depth estimation for robotics: a survey;Dong;IEEE Trans. Intell. Transp. Syst.,2022

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