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홈 뉴스 최신 연구 논문 튜토리얼 데이터셋 백과사전 SOTA LLM 모델 GPU 랭킹 컨퍼런스

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6D Pose Estimation Using Rgb On Occlusion

평가 지표

Mean ADD

평가 결과

이 벤치마크에서 각 모델의 성능 결과

모델 이름	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	GDRNPP: A Geometry-guided and Fully Learning-based Object Pose Estimator	-
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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6D Pose Estimation Using Rgb On Occlusion | SOTA | HyperAI초신경