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

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 in space and time with a fusion function. At each time step, this fusion function makes use of a verification score to quantify the quality of the hypotheses in the absence of ground-truth annotations and passes this information to an agent. The agent reasons about these hypotheses, directing its attention to the object which it is most uncertain about, moving the camera towards such an object. Unlike previous works that propose short-sighted policies, our agent is trained in simulated scenarios using reinforcement learning, attempting to learn the camera moves that produce the most accurate object poses hypotheses for a given temporal and spatial budget, without the need of viewpoints rendering during inference. Our experiments show that the proposed approach successfully estimates the 6D object pose of a stack of objects in both challenging cluttered synthetic and real scenarios, showing superior performance compared to other baselines.