Guided Depth Super-resolution on DIML
4.95MSELearning Graph Regularisation
Evaluation Results
| Method | Links | |||
|---|---|---|---|---|
| Learning Graph RegularisationUpsampling factor=x8, Training dataset=NYUv22022.03 | 4.95 | 0.4 | 0.0024 | |
| MSG-NetUpsampling factor=x8, Training dataset=NYUv22022.03 | 5.76 | 0.51 | 6.16 | |
| FDKNUpsampling factor=x8, Training dataset=NYUv22022.03 | 6.74 | 0.53 | 0.2 | |
| PMBANetUpsampling factor=x8, Training dataset=NYUv22022.03 | 7.35 | 0.59 | 0.04 | |
| FDSRUpsampling factor=x8, Training dataset=NYUv22022.03 | 7.73 | 0.74 | 0.45 | |
| Learning Graph Regularisation (Colour)Upsampling factor=x8, Training dataset=NYUv2, Feature extractor=Colour2022.03 | 20.5 | 0.77 | 0.03 | |
| PixtransformUpsampling factor=x8, Training dataset=NYUv22022.03 | 23 | 1.26 | 6.19 | |
| GFUpsampling factor=x8, Training dataset=NYUv22022.03 | 34.1 | 1.77 | 17.7 | |
| SD filterUpsampling factor=x8, Training dataset=NYUv22022.03 | 44.9 | 0.83 | 1.45 |