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6D Pose Estimation On Ycb Video 2

Metrics

ADDS AUC

Results

Performance results of various models on this benchmark

Paper TitleRepository
ICG+97.9Fusing Visual Appearance and Geometry for Multi-modality 6DoF Object Tracking
FFB6D96.6FFB6D: A Full Flow Bidirectional Fusion Network for 6D Pose Estimation
ICG96.5Iterative Corresponding Geometry: Fusing Region and Depth for Highly Efficient 3D Tracking of Textureless Objects
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
CMCL6D95.43Enhancing 6-DoF Object Pose Estimation through Multiple Modality Fusion: A Hybrid CNN Architecture with Cross-Layer and Cross-Modal Integration-
MaskedFusion93.3MaskedFusion: Mask-based 6D Object Pose Estimation
DenseFusion93.1DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion
PoseCNN+ICP93.0PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes
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6D Pose Estimation On Ycb Video 2 | SOTA | HyperAI