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Models/M4Depth+U

M4Depth+U

Reported on 8 benchmarks across 2 tasks · 1 paper · 6 SOTA

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
RMSE log· 2023-05-31
0.188
SOTA
A technique to jointly estimate depth and depth uncertainty for unmanned aerial vehicles arXiv:2305.19780
Depth EstimationonMid-Air Dataset
AuSE on Abs Rel· 2023-05-31
0.007
SOTA
A technique to jointly estimate depth and depth uncertainty for unmanned aerial vehicles arXiv:2305.19780
Depth EstimationonMid-Air Dataset
AuSE on RMSE log· 2023-05-31
0.02
SOTA
A technique to jointly estimate depth and depth uncertainty for unmanned aerial vehicles arXiv:2305.19780
Depth EstimationonMid-Air Dataset
Abs Rel· 2023-05-31
0.134
best: 0.717 (Monodepth2)
A technique to jointly estimate depth and depth uncertainty for unmanned aerial vehicles arXiv:2305.19780

Methodology4 results

3DonMid-Air Dataset
RMSE log· 2023-05-31
0.188
SOTA
A technique to jointly estimate depth and depth uncertainty for unmanned aerial vehicles arXiv:2305.19780
3DonMid-Air Dataset
AuSE on Abs Rel· 2023-05-31
0.007
SOTA
A technique to jointly estimate depth and depth uncertainty for unmanned aerial vehicles arXiv:2305.19780
3DonMid-Air Dataset
AuSE on RMSE log· 2023-05-31
0.02
SOTA
A technique to jointly estimate depth and depth uncertainty for unmanned aerial vehicles arXiv:2305.19780
3DonMid-Air Dataset
Abs Rel· 2023-05-31
0.134
best: 0.717 (Monodepth2)
A technique to jointly estimate depth and depth uncertainty for unmanned aerial vehicles arXiv:2305.19780