Tasks SotA Datasets Papers Methods Submit About

Papers With Code 2

A community resource for machine learning research: papers, code, benchmarks, and state-of-the-art results.

Explore

Notable Benchmarks All SotA Datasets Papers Methods

Community

Submit Results About

Data sourced from the PWC Archive (CC-BY-SA 4.0). Built by the community, for the community.

Models/Miangoleh et al. (SGR)

Miangoleh et al. (SGR)

Reported on 16 benchmarks across 2 tasks · 1 paper · 10 SOTA

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

Computer Vision8 results

Depth EstimationonIBims-1
RMSE· 2021-05-28
0.1598
SOTA
Boosting Monocular Depth Estimation Models to High-Resolution via Content-Adaptive Multi-Resolution Merging arXiv:2105.14021
Depth EstimationonMiddlebury 2014
D3R· 2021-05-28
0.2324
SOTA
Boosting Monocular Depth Estimation Models to High-Resolution via Content-Adaptive Multi-Resolution Merging arXiv:2105.14021
Depth EstimationonMiddlebury 2014
ORD · 2021-05-28
0.3879
SOTA
Boosting Monocular Depth Estimation Models to High-Resolution via Content-Adaptive Multi-Resolution Merging arXiv:2105.14021
Depth EstimationonMiddlebury 2014
RMSE· 2021-05-28
0.1973
best: 0.1557 (Miangoleh et al. (MiDaS))
SOTA
Boosting Monocular Depth Estimation Models to High-Resolution via Content-Adaptive Multi-Resolution Merging arXiv:2105.14021
Depth EstimationonMiddlebury 2014
δ1.25· 2021-05-28
0.7891
SOTA
Boosting Monocular Depth Estimation Models to High-Resolution via Content-Adaptive Multi-Resolution Merging arXiv:2105.14021
Depth EstimationonIBims-1
D3R· 2021-05-28
0.3222
best: 0.4671 (Miangoleh et al. (MiDaS))
Boosting Monocular Depth Estimation Models to High-Resolution via Content-Adaptive Multi-Resolution Merging arXiv:2105.14021
Depth EstimationonIBims-1
ORD· 2021-05-28
0.3938
best: 0.5538 (Miangoleh et al. (MiDaS))
Boosting Monocular Depth Estimation Models to High-Resolution via Content-Adaptive Multi-Resolution Merging arXiv:2105.14021
Depth EstimationonIBims-1
δ1.25· 2021-05-28
0.639
best: 0.969 (Metric3D-v2(L, ZS))
Boosting Monocular Depth Estimation Models to High-Resolution via Content-Adaptive Multi-Resolution Merging arXiv:2105.14021

Methodology8 results

3DonIBims-1
RMSE· 2021-05-28
0.1598
SOTA
Boosting Monocular Depth Estimation Models to High-Resolution via Content-Adaptive Multi-Resolution Merging arXiv:2105.14021
3DonMiddlebury 2014
D3R· 2021-05-28
0.2324
SOTA
Boosting Monocular Depth Estimation Models to High-Resolution via Content-Adaptive Multi-Resolution Merging arXiv:2105.14021
3DonMiddlebury 2014
ORD · 2021-05-28
0.3879
SOTA
Boosting Monocular Depth Estimation Models to High-Resolution via Content-Adaptive Multi-Resolution Merging arXiv:2105.14021
3DonMiddlebury 2014
RMSE· 2021-05-28
0.1973
best: 0.1557 (Miangoleh et al. (MiDaS))
SOTA
Boosting Monocular Depth Estimation Models to High-Resolution via Content-Adaptive Multi-Resolution Merging arXiv:2105.14021
3DonMiddlebury 2014
δ1.25· 2021-05-28
0.7891
SOTA
Boosting Monocular Depth Estimation Models to High-Resolution via Content-Adaptive Multi-Resolution Merging arXiv:2105.14021
3DonIBims-1
D3R· 2021-05-28
0.3222
best: 0.4671 (Miangoleh et al. (MiDaS))
Boosting Monocular Depth Estimation Models to High-Resolution via Content-Adaptive Multi-Resolution Merging arXiv:2105.14021
3DonIBims-1
ORD· 2021-05-28
0.3938
best: 0.5538 (Miangoleh et al. (MiDaS))
Boosting Monocular Depth Estimation Models to High-Resolution via Content-Adaptive Multi-Resolution Merging arXiv:2105.14021
3DonIBims-1
δ1.25· 2021-05-28
0.639
best: 0.969 (Metric3D-v2(L, ZS))
Boosting Monocular Depth Estimation Models to High-Resolution via Content-Adaptive Multi-Resolution Merging arXiv:2105.14021