SigNet turns sparse depth completion into a depth enhancement problem and reports state-of-the-art accuracy on NYUv2, DIML, SUN RGBD, and TOFDC with a small 3.3M-parameter model.
Learning depth with convolutional spatial propagation network
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.CV 1years
2024 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
Completion as Enhancement: A Degradation-Aware Selective Image Guided Network for Depth Completion
SigNet turns sparse depth completion into a depth enhancement problem and reports state-of-the-art accuracy on NYUv2, DIML, SUN RGBD, and TOFDC with a small 3.3M-parameter model.