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Models/ST-CLSTM

ST-CLSTM

Reported on 8 benchmarks across 2 tasks · 1 paper

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

Computer Vision4 results

Depth EstimationonMid-Air Dataset
Abs Rel· 2019-08-10
0.404
best: 0.717 (Monodepth2)
Exploiting temporal consistency for real-time video depth estimation arXiv:1908.03706
Depth EstimationonMid-Air Dataset
RMSE· 2019-08-10
13.685
best: 8.8641 (M4Depth-d6 (VMD))
Exploiting temporal consistency for real-time video depth estimation arXiv:1908.03706
Depth EstimationonMid-Air Dataset
RMSE log· 2019-08-10
0.4383
best: 0.188 (M4Depth+U)
Exploiting temporal consistency for real-time video depth estimation arXiv:1908.03706
Depth EstimationonMid-Air Dataset
SQ Rel· 2019-08-10
6.3902
best: 37.164 (Monodepth2)
Exploiting temporal consistency for real-time video depth estimation arXiv:1908.03706

Methodology4 results

3DonMid-Air Dataset
Abs Rel· 2019-08-10
0.404
best: 0.717 (Monodepth2)
Exploiting temporal consistency for real-time video depth estimation arXiv:1908.03706
3DonMid-Air Dataset
RMSE· 2019-08-10
13.685
best: 8.8641 (M4Depth-d6 (VMD))
Exploiting temporal consistency for real-time video depth estimation arXiv:1908.03706
3DonMid-Air Dataset
RMSE log· 2019-08-10
0.4383
best: 0.188 (M4Depth+U)
Exploiting temporal consistency for real-time video depth estimation arXiv:1908.03706
3DonMid-Air Dataset
SQ Rel· 2019-08-10
6.3902
best: 37.164 (Monodepth2)
Exploiting temporal consistency for real-time video depth estimation arXiv:1908.03706