HyperAI
HomeNewsLatest PapersTutorialsDatasetsWikiSOTALLM ModelsGPU LeaderboardEvents
Search
About
HyperAI

6D Pose Estimation On Ycb Video 2

Metrics

ADDS AUC

Results

Performance results of various models on this benchmark

Model Name
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
0 of 9 row(s) selected.