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Title: Simultaneous Multi-Level Descriptor Learning and Semantic Segmentation for Domain-Specific Relocalization
This paper presents a semi-supervised framework for multi-level description learning aiming for robust and accurate camera relocalization across large perception variations. Our proposed network, namely DLSSNet, simultaneously learns weakly-supervised semantic segmentation and local feature description in the hierarchy. Therefore, the augmented descriptors, trained in an end-to-end manner, provide a more stable high-level representation for local feature dis-ambiguity. To facilitate end-to-end semantic description learning, the descriptor segmentation module is proposed to jointly learn semantic descriptors and cluster centers using standard semantic segmentation loss. We show that our model can be easily fine-tuned for domain-specific usage without any further semantic annotations, instead, requiring only 2D-2D pixel correspondences. The learned descriptors, trained with our proposed pipeline, can boost the cross-season localization performance against other state-of-the-arts.  more » « less
Award ID(s):
1816138
NSF-PAR ID:
10351886
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
International Conference on Robotics and Automation
Page Range / eLocation ID:
5868 to 5875
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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