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6D Pose Estimation On Ycb Video 2

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ADDS AUC

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

Modellname
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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6D Pose Estimation On Ycb Video 2 | SOTA | HyperAI