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PFRL: Pose-Free Reinforcement Learning for 6D Pose Estimation

CVPR 2020 · 3D vision › Human pose estimation · approach: Instance-level 6D object pose · 37 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Refine AAE initial poses with PFRLAAE · LINEMOD testADD: 31.41 -> 70.1Helped
Refine PoseCNN initial poses with PFRLPoseCNN · LINEMOD testADD: 62.7 -> 79.7Helped
Refine PoseCNN initial poses with PFRL instead of DeepIM-SYNPoseCNN · LINEMOD testProj. 2D: 85.3 -> 94.7Helped
Refine AAE initial poses with PFRLAAE · T-LESS objects 19-23Proj. 2D: 35.12 -> 38.57Helped
Refine AAE initial poses with PFRLAAE · T-LESS objects 19-23VSD: 47.47 -> 51.53Helped
Use discrete instead of continuous actions (ablation)PFRL · LINEMOD BenchviseProj. 2D: 37.6 -> 58.9 (epoch 800); 60.4 -> 80.6 (epoch 3200)Helped
Use discrete instead of continuous actions (ablation)PFRL · LINEMOD BenchviseADD: 43.8 -> 52.8 (epoch 800); 60.9 -> 79.2 (epoch 3200)Helped
Add centralization reward to IoU-difference reward (ablation)PFRL reward · LINEMOD IronIoUHelped
Add goal-reached reward alone to IoU-difference reward (ablation)PFRL reward · LINEMOD IronIoUHurt
Combine goal-reached reward with centralization reward (ablation)PFRL reward · LINEMOD IronIoUHelped
Add off-policy value updates from replay to on-policy PPO (ablation)PFRL optimization · LINEMOD Ape and LampIoUHelped
Train a single class-agnostic model rather than class-specific models (ablation)PFRL · LINEMODADD: 31.41 -> 48.34 mean; class-agnostic scores vary by objectMixed

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