6D Pose Estimation Using Rgb On Occlusion
Métriques
Mean ADD
Résultats
Résultats de performance de divers modèles sur ce benchmark
Tableau comparatif
| Nom du modèle | Mean ADD |
|---|---|
| occlusion-robust-object-pose-estimation-with | 45.95 |
| cullnet-calibrated-and-pose-aware-confidence | 24.48 |
| end-to-end-differentiable-6dof-object-pose | 47.4 |
| dpod-dense-6d-pose-object-detector-in-rgb | 47.25 |
| hybridpose-6d-object-pose-estimation-under | 47.5 |
| segmentation-driven-6d-object-pose-estimation | 27 |
| pvnet-pixel-wise-voting-network-for-6dof-pose | 40.77 |
| repose-real-time-iterative-rendering-and | 51.6 |
| rnnpose-recurrent-6-dof-object-pose | 60.65 |
| gdr-net-geometry-guided-direct-regression | 56.1 |
| deepim-deep-iterative-matching-for-6d-pose | 55.5 |
| pose-proposal-critic-robust-pose-refinement | 55.33 |
| so-pose-exploiting-self-occlusion-for-direct | 62.3 |