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题名

Deep Homography Estimation for Visual Place Recognition

作者
通讯作者Lan, Xiangyuan; Yuan, Chun
DOI
发表日期
2024-03-25
会议名称
38th AAAI Conference on Artificial Intelligence, AAAI 2024
ISSN
2159-5399
EISSN
2374-3468
ISBN
9781577358879
会议录名称
卷号
38
页码
10341-10349
会议日期
February 20, 2024 - February 27, 2024
会议地点
Vancouver, BC, Canada
会议录编者/会议主办者
Association for the Advancement of Artificial Intelligence
出版地
2275 E BAYSHORE RD, STE 160, PALO ALTO, CA 94303 USA
出版者
摘要
Visual place recognition (VPR) is a fundamental task for many applications such as robot localization and augmented reality. Recently, the hierarchical VPR methods have received considerable attention due to the trade-off between accuracy and efficiency. They usually first use global features to retrieve the candidate images, then verify the spatial consistency of matched local features for re-ranking. However, the latter typically relies on the RANSAC algorithm for fitting homography, which is time-consuming and non-differentiable. This makes existing methods compromise to train the network only in global feature extraction. Here, we propose a transformer-based deep homography estimation (DHE) network that takes the dense feature map extracted by a backbone network as input and fits homography for fast and learnable geometric verification. Moreover, we design a re-projection error of inliers loss to train the DHE network without additional homography labels, which can also be jointly trained with the backbone network to help it extract the features that are more suitable for local matching. Extensive experiments on benchmark datasets show that our method can outperform several state-of-the-art methods. And it is more than one order of magnitude faster than the mainstream hierarchical VPR methods using RANSAC. The code is released at https://github.com/Lu-Feng/DHE-VPR.
Copyright © 2024, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
学校署名
其他
语种
英语
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资助项目
This work was supported by the National Key R&D Program of China (2022YFB4701400/4701402), SSTIC Grant (JCYJ20190809172201639, WDZC20200820200655001), Shenzhen Key Laboratory (ZDSYS20210623092001004), the Project of Peng Cheng Laboratory (PCL2023A08), and Beijing Key Lab of Networked Multimedia.
WOS研究方向
Computer Science ; Mathematics ; Robotics
WOS类目
Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods ; Mathematics, Applied ; Robotics
WOS记录号
WOS:001241512400104
EI入藏号
20241515853836
EI主题词
Economic and social effects ; Robot applications
EI分类号
Computer Software, Data Handling and Applications:723 ; Robot Applications:731.6 ; Social Sciences:971
来源库
EV Compendex
引用统计
成果类型会议论文
条目标识符http://sustech.caswiz.com/handle/2SGJ60CL/794529
专题南方科技大学
作者单位
1.Tsinghua Shenzhen International Graduate School, Tsinghua University, China
2.Peng Cheng Laboratory, China
3.Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, China
4.Southern University of Science and Technology, China
推荐引用方式
GB/T 7714
Lu, Feng,Dong, Shuting,Zhang, Lijun,et al. Deep Homography Estimation for Visual Place Recognition[C]//Association for the Advancement of Artificial Intelligence. 2275 E BAYSHORE RD, STE 160, PALO ALTO, CA 94303 USA:Association for the Advancement of Artificial Intelligence,2024:10341-10349.
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