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SOTA
使用RGB进行六维姿态估计
6D Pose Estimation Using Rgb On Occlusion
6D Pose Estimation Using Rgb On Occlusion
评估指标
Mean ADD
评测结果
各个模型在此基准测试上的表现结果
Columns
模型名称
Mean ADD
Paper Title
Repository
SO-Pose
62.3
SO-Pose: Exploiting Self-Occlusion for Direct 6D Pose Estimation
RNNPose (Trained with synthetic data and LINEMOD training set, w/o pbr data)
60.65
RNNPose: Recurrent 6-DoF Object Pose Refinement with Robust Correspondence Field Estimation and Pose Optimization
GDR-Net
56.1
GDRNPP: A Geometry-guided and Fully Learning-based Object Pose Estimator
DeepIM (Train on Occlusion LineMOD)
55.5
DeepIM: Deep Iterative Matching for 6D Pose Estimation
PPC (Refined from initial PVNet pose)
55.33
Pose Proposal Critic: Robust Pose Refinement by Learning Reprojection Errors
-
RePOSE
51.6
RePOSE: Fast 6D Object Pose Refinement via Deep Texture Rendering
HybridPose
47.5
HybridPose: 6D Object Pose Estimation under Hybrid Representations
E2E6DoF
47.4
End-to-End Differentiable 6DoF Object Pose Estimation with Local and Global Constraints
-
DPOD
47.25
DPOD: 6D Pose Object Detector and Refiner
ROPE
45.95
Occlusion-Robust Object Pose Estimation with Holistic Representation
PVNet
40.77
PVNet: Pixel-wise Voting Network for 6DoF Pose Estimation
SegDriven
27
Segmentation-driven 6D Object Pose Estimation
CullNet
24.48
CullNet: Calibrated and Pose Aware Confidence Scores for Object Pose Estimation
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