Guided Depth Super-Resolution on Middlebury (x4, x8, x16 RMSE)
1.02RMSE (x4)SSDNet 2023
Evaluation results
| Method | Links | |||
|---|---|---|---|---|
| SSDNet 2023Training Dataset=NYU v22026.04 | 1.02 | 1.91 | 4.02 | |
| NAIMATraining Dataset=NYU v22026.04 | 1.03 | 1.62 | 2.75 | |
| SUFT 2022Training Dataset=NYU v22026.04 | 1.07 | 1.75 | 3.18 | |
| FDKN 2021Training Dataset=NYU v22026.04 | 1.08 | 2.17 | 4.5 | |
| JIIF 2021Training Dataset=NYU v22026.04 | 1.09 | 1.82 | 3.31 | |
| DCTNet 2022Training Dataset=NYU v22026.04 | 1.1 | 2.05 | 4.19 | |
| FDSR 2021Training Dataset=NYU v22026.04 | 1.13 | 2.08 | 4.39 | |
| DAGF 2023Training Dataset=NYU v22026.04 | 1.15 | 1.8 | 3.7 | |
| SGNet 2024Training Dataset=NYU v22026.04 | 1.15 | 1.64 | 2.95 | |
| DADA 2023Training Dataset=NYU v22026.04 | 1.2 | 2.03 | 4.18 | |
| DKN 2021Training Dataset=NYU v22026.04 | 1.23 | 2.12 | 4.24 |