Surface Normal Estimation on NYUD v2
16.25Mean ErrorSAK
Evaluation Results
| Method | Links | |||
|---|---|---|---|---|
| SAKCategory=Conventional Multi-task Methods2026.06 | 16.25 | — | — | |
| TIGER-LBackbone=ViT-L2026.06 | 16.8 | — | — | |
| TIGER-BBackbone=ViT-B2026.06 | 16.95 | — | — | |
| MLoRECategory=Conventional Multi-task Methods2026.06 | 18.33 | — | — | |
| SEMCategory=Conventional Multi-task Methods2026.06 | 18.45 | — | — | |
| TaskPrompterCategory=Conventional Multi-task Methods2026.06 | 18.47 | — | — | |
| RADIOCategory=Conventional Multi-task Methods2026.06 | 18.49 | — | — | |
| TaskExpertCategory=Conventional Multi-task Methods2026.06 | 18.54 | — | — | |
| MTMambaCategory=Conventional Multi-task Methods2026.06 | 18.63 | — | — | |
| InvPTCategory=Conventional Multi-task Methods2026.06 | 19.04 | — | — | |
| DINOv2-ViT-LBackbone=ViT-L2026.06 | 19.23 | — | — | |
| MQTransformerCategory=Conventional Multi-task Methods2026.06 | 19.67 | — | — | |
| ATRCCategory=Conventional Multi-task Methods2026.06 | 20.18 | — | — | |
| MTI-NetCategory=Conventional Multi-task Methods2026.06 | 20.27 | — | — | |
| PAD-NetCategory=Conventional Multi-task Methods2026.06 | 20.88 | — | — | |
| SAM-ViT-BBackbone=ViT-B2026.06 | 23.59 | — | — | |
| DiffusionMTLCategory=Conventional Multi-task Methods2026.06 | 24.75 | — | — | |
| OWLv2-ViT-LBackbone=ViT-L2026.06 | 25.2 | — | — | |
| CLIP-ViT-LBackbone=ViT-L2026.06 | 26.51 | — | — | |
| DiTASKBackbone=Swin-Tiny, Pre-trained=ImageNet-1k, Trainable Parameters (M)=3.062026.03 | — | 27.25 | -10.45 | |
| FAARBackbone=Swin-Tiny, Pre-trained=ImageNet-1k, Trainable Parameters (M)=2.85, Rank (rinit)=642026.03 | — | 26.35 | -7.88 | |
| MTL - Full Fine TuningBackbone=Swin-Tiny, Pre-trained=ImageNet-1k, Trainable Parameters (M)=28.102026.03 | — | 24.33 | -8.49 | |
| MTLoRABackbone=Swin-Tiny, Pre-trained=ImageNet-1k, Trainable Parameters (M)=9.70, Rank (r)=642026.03 | — | 29.53 | -15.3 | |
| Single TaskBackbone=Swin-Tiny, Pre-trained=ImageNet-1k, Trainable Parameters (M)=84.002026.03 | — | 22.83 | 0 | |
| TADFormerBackbone=Swin-Tiny, Pre-trained=ImageNet-1k, Trainable Parameters (M)=8.90, Rank (r)=642026.03 | — | 27.48 | -10.42 |