NLFB

Reported on 4 benchmarks across 2 tasks · 1 paper · 4 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Computer Vision2 results

Depth EstimationonStanford2D3D Panoramic
RMSE· 2021-09-22
0.2776
best: 0.2619 (HiMODE)
SOTA
Improving 360 Monocular Depth Estimation via Non-local Dense Prediction Transformer and Joint Supervised and Self-supervised Learning arXiv:2109.10563
Depth EstimationonStanford2D3D Panoramic
absolute relative error· 2021-09-22
0.0649
best: 0.0405 (PanoFormer)
SOTA
Improving 360 Monocular Depth Estimation via Non-local Dense Prediction Transformer and Joint Supervised and Self-supervised Learning arXiv:2109.10563

Methodology2 results

3DonStanford2D3D Panoramic
RMSE· 2021-09-22
0.2776
best: 0.2619 (HiMODE)
SOTA
Improving 360 Monocular Depth Estimation via Non-local Dense Prediction Transformer and Joint Supervised and Self-supervised Learning arXiv:2109.10563
3DonStanford2D3D Panoramic
absolute relative error· 2021-09-22
0.0649
best: 0.0405 (PanoFormer)
SOTA
Improving 360 Monocular Depth Estimation via Non-local Dense Prediction Transformer and Joint Supervised and Self-supervised Learning arXiv:2109.10563