PanoDepth

Reported on 4 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 Vision2 results

Depth EstimationonStanford2D3D Panoramic
RMSE· 2022-02-02
0.3747
best: 0.2619 (HiMODE)
PanoDepth: A Two-Stage Approach for Monocular Omnidirectional Depth Estimation arXiv:2202.01323
Depth EstimationonStanford2D3D Panoramic
absolute relative error· 2022-02-02
0.0972
best: 0.0405 (PanoFormer)
PanoDepth: A Two-Stage Approach for Monocular Omnidirectional Depth Estimation arXiv:2202.01323

Methodology2 results

3DonStanford2D3D Panoramic
RMSE· 2022-02-02
0.3747
best: 0.2619 (HiMODE)
PanoDepth: A Two-Stage Approach for Monocular Omnidirectional Depth Estimation arXiv:2202.01323
3DonStanford2D3D Panoramic
absolute relative error· 2022-02-02
0.0972
best: 0.0405 (PanoFormer)
PanoDepth: A Two-Stage Approach for Monocular Omnidirectional Depth Estimation arXiv:2202.01323