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홈뉴스연구 논문튜토리얼데이터셋백과사전SOTALLM 모델GPU 랭킹컨퍼런스
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6D Pose Estimation On Ycb Video 2

평가 지표

ADDS AUC

평가 결과

이 벤치마크에서 각 모델의 성능 결과

모델 이름
ADDS AUC
Paper TitleRepository
ICG96.5Iterative Corresponding Geometry: Fusing Region and Depth for Highly Efficient 3D Tracking of Textureless Objects
PoseCNN+ICP93.0PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes
FFB6D96.6FFB6D: A Full Flow Bidirectional Fusion Network for 6D Pose Estimation
ICG+97.9Fusing Visual Appearance and Geometry for Multi-modality 6DoF Object Tracking
CMCL6D95.43Enhancing 6-DoF Object Pose Estimation through Multiple Modality Fusion: A Hybrid CNN Architecture with Cross-Layer and Cross-Modal Integration-
DenseFusion93.1DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion
MaskedFusion93.3MaskedFusion: Mask-based 6D Object Pose Estimation
PVN3D96.1PVN3D: A Deep Point-wise 3D Keypoints Voting Network for 6DoF Pose Estimation
se3-TrackNet95.71se(3)-TrackNet: Data-driven 6D Pose Tracking by Calibrating Image Residuals in Synthetic Domains
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