Unsupervised deep depth completion with heterogeneous LiDAR and RGB-D camera depth information

In this work, a depth-only completion method designed to enhance perception in light-deprived environments. We achieve this through LidarDepthNet, a novel end-to-end unsupervised learning framework that fuses heterogeneous depth information captured by two distinct depth sensors: LiDAR and RGB-D cam...

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Autori principali:	Guohua Gou, Han Li, Xuanhao Wang, Hao Zhang, Wei Yang, Haigang Sui
Natura:	Artigo
Lingua:	Inglês
Pubblicazione:	Elsevier 2025-02-01
Serie:	International Journal of Applied Earth Observations and Geoinformation
Soggetti:	Depth completion Depth-only inputs Heterogeneous depth information Unsupervised deep learning Temporal depth consistency loss Depth measurement confidence
Accesso online:	http://www.sciencedirect.com/science/article/pii/S156984322400685X
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