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SOTA
Détection d'objets 3D à partir d'images monoculaires
3D Object Detection From Monocular Images On 7
3D Object Detection From Monocular Images On 7
Métriques
AP25
AP50
Résultats
Résultats de performance de divers modèles sur ce benchmark
Columns
Nom du modèle
AP25
AP50
Paper Title
Repository
MonoDETR
27.13
0.79
MonoDETR: Depth-guided Transformer for Monocular 3D Object Detection
-
MonoDLE
28.99
0.85
Delving into Localization Errors for Monocular 3D Object Detection
-
VoteNet
30.61
3.40
Deep Hough Voting for 3D Object Detection in Point Clouds
-
SeaBird + Image2Maps
35.04
3.14
SeaBird: Segmentation in Bird's View with Dice Loss Improves Monocular 3D Detection of Large Objects
-
MonoDTR
39.76
3.02
MonoDTR: Monocular 3D Object Detection with Depth-Aware Transformer
-
SeaBird + PanopticBEV
37.12
4.64
SeaBird: Segmentation in Bird's View with Dice Loss Improves Monocular 3D Detection of Large Objects
-
Cube R-CNN
15.57
0.80
Omni3D: A Large Benchmark and Model for 3D Object Detection in the Wild
-
GrooMeD-NMS
16.12
0.17
GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection
-
BoxNet
23.59
4.08
Deep Hough Voting for 3D Object Detection in Point Clouds
-
GUPNet
27.25
0.87
Geometry Uncertainty Projection Network for Monocular 3D Object Detection
-
DEVIANT
26.96
0.88
DEVIANT: Depth EquiVarIAnt NeTwork for Monocular 3D Object Detection
-
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