Active 6D Multi-Object Pose Estimation in Cluttered Scenarios with Deep Reinforcement Learning
Juil Sock,Guillermo Garcia-Hernando,Tae-Kyun Kim,Juil Sock,Guillermo Garcia-Hernando,Tae-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 such as time and distance travelled, important in robotics and augmented reality applications. In the proposed framework, multiple object hypotheses inferred by an object pose estimator are accumulated both ...
