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SOTA
6D Pose Estimation 1
6D Pose Estimation On Ycb Video 2
6D Pose Estimation On Ycb Video 2
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ADDS AUC
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Columns
Modellname
ADDS AUC
Paper Title
Repository
ICG
96.5
Iterative Corresponding Geometry: Fusing Region and Depth for Highly Efficient 3D Tracking of Textureless Objects
PoseCNN+ICP
93.0
PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes
FFB6D
96.6
FFB6D: A Full Flow Bidirectional Fusion Network for 6D Pose Estimation
ICG+
97.9
Fusing Visual Appearance and Geometry for Multi-modality 6DoF Object Tracking
CMCL6D
95.43
Enhancing 6-DoF Object Pose Estimation through Multiple Modality Fusion: A Hybrid CNN Architecture with Cross-Layer and Cross-Modal Integration
DenseFusion
93.1
DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion
MaskedFusion
93.3
MaskedFusion: Mask-based 6D Object Pose Estimation
PVN3D
96.1
PVN3D: A Deep Point-wise 3D Keypoints Voting Network for 6DoF Pose Estimation
se3-TrackNet
95.71
se(3)-TrackNet: Data-driven 6D Pose Tracking by Calibrating Image Residuals in Synthetic Domains
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