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Models/DFI-OmniStereo

DFI-OmniStereo

Reported on 24 benchmarks across 3 tasks · 1 paper · 15 SOTA

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

Computer Vision16 results

Depth EstimationonHelvipad
Depth-MAE· 2025-03-30
1.463
SOTA
Boosting Omnidirectional Stereo Matching with a Pre-trained Depth Foundation Model arXiv:2503.23502
Depth EstimationonHelvipad
Depth-RMSE· 2025-03-30
3.767
SOTA
Boosting Omnidirectional Stereo Matching with a Pre-trained Depth Foundation Model arXiv:2503.23502
Depth EstimationonHelvipad
Disp-LRCE· 2025-03-30
0.058
SOTA
Boosting Omnidirectional Stereo Matching with a Pre-trained Depth Foundation Model arXiv:2503.23502
Depth EstimationonHelvipad
Disp-MAE· 2025-03-30
0.158
SOTA
Boosting Omnidirectional Stereo Matching with a Pre-trained Depth Foundation Model arXiv:2503.23502
Depth EstimationonHelvipad
Disp-RMSE· 2025-03-30
0.338
SOTA
Boosting Omnidirectional Stereo Matching with a Pre-trained Depth Foundation Model arXiv:2503.23502
Stereo Depth EstimationonHelvipad
Depth-MAE· 2025-03-30
1.463
SOTA
Boosting Omnidirectional Stereo Matching with a Pre-trained Depth Foundation Model arXiv:2503.23502
Stereo Depth EstimationonHelvipad
Depth-RMSE· 2025-03-30
3.767
SOTA
Boosting Omnidirectional Stereo Matching with a Pre-trained Depth Foundation Model arXiv:2503.23502
Stereo Depth EstimationonHelvipad
Disp-LRCE· 2025-03-30
0.058
SOTA
Boosting Omnidirectional Stereo Matching with a Pre-trained Depth Foundation Model arXiv:2503.23502
Stereo Depth EstimationonHelvipad
Disp-MAE· 2025-03-30
0.158
SOTA
Boosting Omnidirectional Stereo Matching with a Pre-trained Depth Foundation Model arXiv:2503.23502
Stereo Depth EstimationonHelvipad
Disp-RMSE· 2025-03-30
0.338
SOTA
Boosting Omnidirectional Stereo Matching with a Pre-trained Depth Foundation Model arXiv:2503.23502
Depth EstimationonHelvipad
Depth-LRCE· 2025-03-30
0.397
best: 1.809 (PSMNet)
Boosting Omnidirectional Stereo Matching with a Pre-trained Depth Foundation Model arXiv:2503.23502
Depth EstimationonHelvipad
Depth-MARE· 2025-03-30
0.108
best: 0.176 (PSMNet)
Boosting Omnidirectional Stereo Matching with a Pre-trained Depth Foundation Model arXiv:2503.23502
Depth EstimationonHelvipad
Disp-MARE· 2025-03-30
0.12
best: 0.248 (PSMNet)
Boosting Omnidirectional Stereo Matching with a Pre-trained Depth Foundation Model arXiv:2503.23502
Stereo Depth EstimationonHelvipad
Depth-LRCE· 2025-03-30
0.397
best: 1.809 (PSMNet)
Boosting Omnidirectional Stereo Matching with a Pre-trained Depth Foundation Model arXiv:2503.23502
Stereo Depth EstimationonHelvipad
Depth-MARE· 2025-03-30
0.108
best: 0.176 (PSMNet)
Boosting Omnidirectional Stereo Matching with a Pre-trained Depth Foundation Model arXiv:2503.23502
Stereo Depth EstimationonHelvipad
Disp-MARE· 2025-03-30
0.12
best: 0.248 (PSMNet)
Boosting Omnidirectional Stereo Matching with a Pre-trained Depth Foundation Model arXiv:2503.23502

Methodology8 results

3DonHelvipad
Depth-MAE· 2025-03-30
1.463
SOTA
Boosting Omnidirectional Stereo Matching with a Pre-trained Depth Foundation Model arXiv:2503.23502
3DonHelvipad
Depth-RMSE· 2025-03-30
3.767
SOTA
Boosting Omnidirectional Stereo Matching with a Pre-trained Depth Foundation Model arXiv:2503.23502
3DonHelvipad
Disp-LRCE· 2025-03-30
0.058
SOTA
Boosting Omnidirectional Stereo Matching with a Pre-trained Depth Foundation Model arXiv:2503.23502
3DonHelvipad
Disp-MAE· 2025-03-30
0.158
SOTA
Boosting Omnidirectional Stereo Matching with a Pre-trained Depth Foundation Model arXiv:2503.23502
3DonHelvipad
Disp-RMSE· 2025-03-30
0.338
SOTA
Boosting Omnidirectional Stereo Matching with a Pre-trained Depth Foundation Model arXiv:2503.23502
3DonHelvipad
Depth-LRCE· 2025-03-30
0.397
best: 1.809 (PSMNet)
Boosting Omnidirectional Stereo Matching with a Pre-trained Depth Foundation Model arXiv:2503.23502
3DonHelvipad
Depth-MARE· 2025-03-30
0.108
best: 0.176 (PSMNet)
Boosting Omnidirectional Stereo Matching with a Pre-trained Depth Foundation Model arXiv:2503.23502
3DonHelvipad
Disp-MARE· 2025-03-30
0.12
best: 0.248 (PSMNet)
Boosting Omnidirectional Stereo Matching with a Pre-trained Depth Foundation Model arXiv:2503.23502