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6D Pose Estimation Using Rgb On Occlusion

Métriques

Mean ADD

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	Mean ADD	Paper Title	Repository
ROPE	45.95	Occlusion-Robust Object Pose Estimation with Holistic Representation
CullNet	24.48	CullNet: Calibrated and Pose Aware Confidence Scores for Object Pose Estimation
E2E6DoF	47.4	End-to-End Differentiable 6DoF Object Pose Estimation with Local and Global Constraints	-
DPOD	47.25	DPOD: 6D Pose Object Detector and Refiner
HybridPose	47.5	HybridPose: 6D Object Pose Estimation under Hybrid Representations
SegDriven	27	Segmentation-driven 6D Object Pose Estimation
PVNet	40.77	PVNet: Pixel-wise Voting Network for 6DoF Pose Estimation
RePOSE	51.6	RePOSE: Fast 6D Object Pose Refinement via Deep Texture Rendering
RNNPose (Trained with synthetic data and LINEMOD training set, w/o pbr data)	60.65	RNNPose: Recurrent 6-DoF Object Pose Refinement with Robust Correspondence Field Estimation and Pose Optimization	-
GDR-Net	56.1	GDR-Net: Geometry-Guided Direct Regression Network for Monocular 6D Object Pose Estimation
DeepIM (Train on Occlusion LineMOD)	55.5	DeepIM: Deep Iterative Matching for 6D Pose Estimation
PPC (Refined from initial PVNet pose)	55.33	Pose Proposal Critic: Robust Pose Refinement by Learning Reprojection Errors	-
SO-Pose	62.3	SO-Pose: Exploiting Self-Occlusion for Direct 6D Pose Estimation

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