Active 6D Multi-Object Pose Estimation in Cluttered Scenarios with Deep Reinforcement Learning

19 Oct 2019Juil SockGuillermo Garcia-HernandoTae-Kyun Kim

In this work, we explore how a strategic selection of camera movements can facilitate the task of 6D multi-object pose estimation in cluttered scenarios while respecting real-world constraints important in robotics and augmented reality applications, such as time and distance traveled. In the proposed framework, a set of multiple object hypotheses is given to an agent, which is inferred by an object pose estimator and subsequently spatio-temporally selected by a fusion function that makes use of a verification score that circumvents the need of ground-truth annotations... (read more)

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