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3D Object Detection On Dair V2X I

Métriques

AP|R40(easy)

AP|R40(hard)

AP|R40(moderate)

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	AP\|R40(easy)	AP\|R40(hard)	AP\|R40(moderate)	Paper Title	Repository
MonoUNI	90.92	87.2	87.2	MonoUNI: A Unified Vehicle and Infrastructure-side Monocular 3D Object Detection Network with Sufficient Depth Clues
PointPillars	63.1	54.0	54.0	PointPillars: Fast Encoders for Object Detection from Point Clouds	-
BEVHeight	77.8	65.9	65.8	BEVHeight: A Robust Framework for Vision-based Roadside 3D Object Detection	-
MVXNet	71.0	53.8	53.7	MVX-Net: Multimodal VoxelNet for 3D Object Detection	-
CBR	72.0	60.1	60.1	Calibration-free BEV Representation for Infrastructure Perception	-
CoBEV	82.0	69.7	69.6	CoBEV: Elevating Roadside 3D Object Detection with Depth and Height Complementarity	-
BEVFormer	61.4	50.7	50.7	BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers	-
BEVDepth	75.7	63.7	63.6	BEVDepth: Acquisition of Reliable Depth for Multi-view 3D Object Detection	-
ImVoxelNet	44.8	37.6	37.6	ImVoxelNet: Image to Voxels Projection for Monocular and Multi-View General-Purpose 3D Object Detection	-

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3D Object Detection On Dair V2X I | SOTA | HyperAI