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...
Main Authors: | , , , , |
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Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2023-01-01
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Series: | PLoS ONE |
Online Access: | https://doi.org/10.1371/journal.pone.0280886 |