REAPER: ARTICULATED OBJECT 6D POSE ESTIMATION WITH DEEP REINFORCEMENT LEARNING
Liu Liu, Qi Wu, Zhendong Xue, Sucheng Qian, Rui Li
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Current articulated object pose estimation methods largely rely on dense prediction for all the input observed point cloud that suffers from huge computational costs and inference time. Besides, self-occlusion is also becoming a key problem that limits the pose estimation performance for those child parts. To solve these issues, we propose a Reinforcement learning based Articulation Pose EstimatoR (ReAPER), which integrates RL into deep neural network for per-part pose estimation. Specifically, we design the novel action space that involves the object's rotation and translation, as well as a reward function considering chamfer distance during pose fitting. To speed up the RL policy training, we employ imitation learning for policy initialization. Finally, we also introduce a new kinematic energy function to optimize the child parts' poses. Experimental results show that ReAPER could obtain state-of-the-art performance on articulated object pose estimation task.