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Document type:
Konferenzbeitrag 
Contribution type:
Elektronisches Dokument 
Author(s):
Stefano Gasperini; Patrick Koch; Vinzenz Dallabetta; Nassir Navab; Benjamin Busam; Federico Tombari 
Title:
R4Dyn: Exploring Radar for Self-Supervised Monocular Depth Estimation of Dynamic Scenes 
Abstract:
While self-supervised monocular depth estimation in driving scenarios has achieved comparable performance to supervised approaches, violations of the static world assumption can still lead to erroneous depth predictions of traffic participants, posing a potential safety issue. In this paper, we present R4Dyn, a novel set of techniques to use cost-efficient radar data on top of a self-supervised depth estimation framework. In particular, we show how radar can be used during training as weak super...    »
 
Keywords:
depth estimation; self-supervised; radar; autonomous driving; deep learning; machine learning 
Dewey Decimal Classification:
000 Informatik, Wissen, Systeme 
Book / Congress title:
Proceedings of the IEEE International Conference on 3D Vision (3DV), 2021 
Year:
2021 
Month:
Dec 
Pages:
751-760 
Reviewed:
ja 
Notes:
The first two authors contributed equally.