PoseAgent: Budget-Constrained 6D Object Pose Estimation
via Reinforcement Learning
Abstract
State-of-the-art computer vision algorithms often
achieve efficiency by making discrete choices about which
hypotheses to explore next. This allows allocation of
computational resources to promising candidates, however,
such decisions are non-differentiable. As a result, these
algorithms are hard to train in an end-to-end fashion.
In this work we propose to learn an efficient algorithm
for the task of 6D object pose estimation. Our system
optimizes the parameters of an existing state-of-the art pose
estimation system using reinforcement learning, where
the pose estimation system now becomes the stochastic
policy, parametrized by a CNN. Additionally, we present
an efficient training algorithm that dramatically reduces
computation time. We show empirically that our learned
pose estimation procedure makes better use of limited
resources and improves upon the state-of-the-art on a
challenging dataset. Our approach enables differentiable
end-to-end training of complex algorithmic pipelines and
learns to make optimal use of a given computational
budget.