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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
Fulltext / DOI:
doi:10.1109/3DV53792.2021.00084
Notes:
The first two authors contributed equally.
Copyright statement:
Copyright with IEEE.
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