Monocular Depth Estimation on 360D (test)
0.1113RMSERectNet
Evaluation results
| Method | Links | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| RectNetEvaluation Mode=Per cube face2018.07 | 0.1113 | 0.008 | 0.0042 | 0.035 | 94.97 | 99.07 | 99.69 | — | — | |
| UResNetEvaluation Mode=Per cube face2018.07 | 0.1289 | 0.0097 | 0.0062 | 0.041 | 92.45 | 98.53 | 99.55 | — | — | |
| SliceNetMethod architecture=standard2022.02 | 0.1323 | — | — | 0.0212 | 97.88 | 99.52 | 99.69 | 0.1134 | — | |
| SliceNet2022.08 | 0.1323 | — | — | 0.0212 | 97.88 | 99.52 | 99.69 | 0.1134 | 0.0467 | |
| ODE-CNNadditional sensor=depth sensor2022.02 | 0.1728 | 0.0467 | — | — | 98.14 | 99.67 | 99.89 | — | — | |
| Distortion-aware2022.02 | 0.1769 | 0.0406 | — | — | 98.65 | 99.66 | 99.87 | — | — | |
| GLPanoDepth(Complete)Method architecture=Full architecture2022.02 | 0.1844 | — | — | 0.0313 | 98.34 | 99.65 | 99.87 | 0.0978 | — | |
| PanoDepthsynthesized views=3, cascade design=two-level2022.02 | 0.1955 | 0.0456 | — | — | 98.3 | 99.57 | 99.84 | — | — | |
| GLPanoDepth(Concat)Method architecture=Concatenation fusion2022.02 | 0.2011 | — | — | 0.0344 | 97.86 | 99.61 | 99.87 | 0.1104 | — | |
| GLPanoDepth(ViT+CNN)Method architecture=ViT and CNN hybrid2022.02 | 0.2316 | — | — | 0.0392 | 96.81 | 99.46 | 99.83 | 0.1279 | — | |
| SphereDepth2022.08 | 0.2364 | — | — | 0.0369 | 97.43 | 99.44 | 99.78 | 0.1145 | 0.055 | |
| BiFuse with fusion2022.02 | 0.244 | 0.0615 | — | — | 96.99 | 99.27 | 99.69 | — | — | |
| BiFuseMethod architecture=standard2022.02 | 0.244 | — | — | 0.0428 | 96.99 | 99.27 | 99.69 | 0.1143 | — | |
| BiFuse2022.08 | 0.244 | — | — | 0.0428 | 96.99 | 99.27 | 99.69 | 0.1143 | 0.0615 | |
| FCRN2022.02 | 0.2833 | 0.0699 | — | — | 95.32 | 99.05 | 99.66 | — | — | |
| FCRNMethod architecture=standard2022.02 | 0.2833 | — | — | 0.0473 | 95.32 | 99.05 | 99.66 | 0.1381 | — | |
| FCRN2022.08 | 0.2833 | — | — | 0.0473 | 95.32 | 99.05 | 99.66 | 0.1381 | 0.0699 | |
| RectNet2022.02 | 0.2911 | 0.0702 | — | — | 95.74 | 99.33 | 99.79 | — | — | |
| Mapped Convolution2022.02 | 0.2966 | 0.0965 | — | — | 90.68 | 98.54 | 99.67 | — | — | |
| Liu et al.Evaluation Mode=Per cube face2018.07 | 0.3048 | 0.0312 | 0.0532 | 0.107 | 60.3 | 84.12 | 93.38 | — | — | |
| Laina et al.Evaluation Mode=Per cube face2018.07 | 0.3152 | 0.03 | 0.0549 | 0.1033 | 63.53 | 86.16 | 94.12 | — | — | |
| OmniDepthMethod architecture=standard2022.02 | 0.3171 | — | — | 0.0725 | 90.92 | 97.02 | 98.51 | 0.1706 | — | |
| OmniDepth2022.08 | 0.3171 | — | — | 0.0725 | 90.92 | 97.02 | 98.51 | 0.1706 | 0.0931 | |
| GLPanoDepth(CViT)Method architecture=CViT only2022.02 | 0.3555 | — | — | 0.0604 | 92.19 | 98.51 | 99.51 | 0.1791 | — | |
| Godard et al.Evaluation Mode=Per cube face2018.07 | 1.6559 | 0.0453 | 0.1743 | 0.1958 | 45.24 | 70.23 | 83.15 | — | — |