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SPNet: Structure preserving network for depth completion

Depth completion aims to predict a dense depth map from a sparse one. Benefiting from the powerful ability of convolutional neural networks, recent depth completion methods have achieved remarkable performance. However, it is still a challenging problem to well preserve accurate depth structures, su...

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Detalles Bibliográficos
Autores principales: Li, Tao, Luo, Songning, Fan, Zhiwei, Zhou, Qunbing, Hu, Ting
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9873174/
https://www.ncbi.nlm.nih.gov/pubmed/36693066
http://dx.doi.org/10.1371/journal.pone.0280886