SporeAgent: Reinforced Scene-level Plausibility for Object Pose Refinement
- Publisher:
- IEEE
- Publication Type:
- Conference Proceeding
- Citation:
- 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2022, 00, pp. 196-204
- Issue Date:
- 2022-02-15
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| SporeAgent_Reinforced_Scene-level_Plausibility_for_Object_Pose_Refinement.pdf | Published version | 3.86 MB |
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Observational noise inaccurate segmentation and ambiguity due to symmetry and occlusion lead to inaccurate object pose estimates While depth and RGB based pose refinement approaches increase the accuracy of the resulting pose estimates they are susceptible to ambiguity in the observation as they consider visual alignment We propose to leverage the fact that we often observe static rigid scenes Thus the objects therein need to be under physically plausible poses We show that considering plausibility reduces ambiguity and in consequence allows poses to be more accurately predicted in cluttered environments To this end we extend a recent RL based registration approach towards iterative refinement of object poses Experiments on the LINEMOD and YCB VIDEO datasets demonstrate the state of the art performance of our depth based refinement approach Code is available at github com dornik sporeagent
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