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3D Object Detection From Monocular Images On 7

Metrics

AP25

AP50

Results

Performance results of various models on this benchmark

			Paper Title	Repository
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
SeaBird + Image2Maps	35.04	3.14	SeaBird: Segmentation in Bird's View with Dice Loss Improves Monocular 3D Detection of Large Objects
VoteNet	30.61	3.40	Deep Hough Voting for 3D Object Detection in Point Clouds
MonoDLE	28.99	0.85	Delving into Localization Errors for Monocular 3D Object Detection
GUPNet	27.25	0.87	Geometry Uncertainty Projection Network for Monocular 3D Object Detection
MonoDETR	27.13	0.79	MonoDETR: Depth-guided Transformer for Monocular 3D Object Detection
DEVIANT	26.96	0.88	DEVIANT: Depth EquiVarIAnt NeTwork for Monocular 3D Object Detection
BoxNet	23.59	4.08	Deep Hough Voting for 3D Object Detection in Point Clouds
GrooMeD-NMS	16.12	0.17	GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection
Cube R-CNN	15.57	0.80	Omni3D: A Large Benchmark and Model for 3D Object Detection in the Wild

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3D Object Detection From Monocular Images On 7 | SOTA | HyperAI