  6D pose estimation from a single RGB image is a challenging and vital task in
computer vision. The current mainstream deep model methods resort to 2D images
annotated with real-world ground-truth 6D object poses, whose collection is
fairly cumbersome and expensive, even unavailable in many cases. In this work,
to get rid of the burden of 6D annotations, we formulate the 6D pose refinement
as a Markov Decision Process and impose on the reinforcement learning approach
with only 2D image annotations as weakly-supervised 6D pose information, via a
delicate reward definition and a composite reinforced optimization method for
efficient and effective policy training. Experiments on LINEMOD and T-LESS
datasets demonstrate that our Pose-Free approach is able to achieve
state-of-the-art performance compared with the methods without using real-world
ground-truth 6D pose labels.
