List your company
ResearchFormerly WizWand

Depth Map Super-Resolution on RGB-D-D (test)

1.11RMSEFDSR+
0.82322.75914.6956.6309Apr 13, 2021Oct 4, 2021Mar 27, 2022Sep 17, 2022Mar 10, 2023Aug 31, 2023Feb 21, 2024
Updated 7mo ago

Evaluation results

MethodLinks
FDSR+Scale factor=x4, Training data=RGB-D-D, Training manner=downsampling2021.041.11——
FDSRScale factor=x4, Training data=NYU v22021.041.16——
FDKNScale factor=x4, Training data=NYU v22021.041.18——
DKNScale factor=x4, Training data=NYU v22021.041.3——
FDSR+Scale factor=x8, Training data=RGB-D-D, Training manner=downsampling2021.041.71——
FDSRScale factor=x8, Training data=NYU v22021.041.82——
FDKNScale factor=x8, Training data=NYU v22021.041.91——
DKNScale factor=x8, Training data=NYU v22021.041.96——
SDFScale factor=x4, Training data=NYU v22021.042——
FDSR+Scale factor=x16, Training data=RGB-D-D, Training manner=downsampling2021.043.01——
FDSRScale factor=x16, Training data=NYU v22021.043.06——
SDFScale factor=x8, Training data=NYU v22021.043.23——
DJFRScale factor=x4, Training data=NYU v22021.043.35——
SVLRMScale factor=x4, Training data=NYU v22021.043.39——
DJFScale factor=x4, Training data=NYU v22021.043.41——
FDKNScale factor=x16, Training data=NYU v22021.043.41——
DKNScale factor=x16, Training data=NYU v22021.043.42——
SPFNetScale Factor=x32, Downsampling Method=bicubic2024.023.97——
SPFNet-DScale Factor=x32, Downsampling Method=bicubic2024.024.13——
SPFNet-TScale Factor=x32, Downsampling Method=bicubic2024.024.8——
DORNetScale Factor=x32, Downsampling Method=bicubic2024.025.05——
FDSRScale Factor=x32, Downsampling Method=bicubic2024.025.08——
SGNetScale Factor=x32, Downsampling Method=bicubic2024.025.15——
SDFScale factor=x16, Training data=NYU v22021.045.16——
FDSR++Scale=x4, Training Manner=real-world training manner2021.045.491.7142.89
DJFScale factor=x8, Training data=NYU v22021.045.57——
DJFRScale factor=x8, Training data=NYU v22021.045.57——
SVLRMScale factor=x8, Training data=NYU v22021.045.59——
DKNScale Factor=x32, Downsampling Method=bicubic2024.025.97——
DCTNetScale Factor=x32, Downsampling Method=bicubic2024.025.99——
DJFRScale Factor=x32, Downsampling Method=bicubic2024.026.48——
SDFScale=x42021.047.162.8652.78
DKNScale=x42021.047.382.8351.9
FDKNScale=x42021.047.52.8551.73
FDSRScale=x42021.047.52.951.89
CUNetScale Factor=x32, Downsampling Method=bicubic2024.027.75——
DJFScale=x42021.047.93.6250.56
DJFRScale factor=x16, Training data=NYU v22021.047.99——
DJFRScale=x42021.048.013.6752.28
SVLRMScale=x42021.048.053.6251.87
DJFScale factor=x16, Training data=NYU v22021.048.15——
SVLRMScale factor=x16, Training data=NYU v22021.048.28——
DJFUpscaling factor=x42021.04—0.825.65
DJFUpscaling factor=x82021.04—2.1917.07
DJFUpscaling factor=x162021.04—4.8935.32
DJFRUpscaling factor=x42021.04—0.795.26
DJFRUpscaling factor=x82021.04—2.1515.66
DJFRUpscaling factor=x162021.04—4.7834.54
DKNUpscaling factor=x42021.04—0.142.11
DKNUpscaling factor=x82021.04—0.333.55
DKNUpscaling factor=x162021.04—1.5412.93
FDKNUpscaling factor=x42021.04—0.111.39
FDKNUpscaling factor=x82021.04—0.283.41
FDKNUpscaling factor=x162021.04—0.9411.73
FDSRUpscaling factor=x4, Training manner=real-world training2021.04—0.11.38
FDSRUpscaling factor=x8, Training manner=real-world training2021.04—0.263.09
FDSRUpscaling factor=x16, Training manner=real-world training2021.04—0.7612.47
FDSR+Upscaling factor=x4, Training manner=downsampling training2021.04—0.091.15
FDSR+Upscaling factor=x8, Training manner=downsampling training2021.04—0.212.79
FDSR+Upscaling factor=x16, Training manner=downsampling training2021.04—0.6711.68
SDFUpscaling factor=x42021.04—0.333.22
SDFUpscaling factor=x82021.04—0.98.74
SDFUpscaling factor=x162021.04—2.3720.71
SVLRMUpscaling factor=x42021.04—0.85.08
SVLRMUpscaling factor=x82021.04—2.1115.18
SVLRMUpscaling factor=x162021.04—4.5834.3