  In this work, we introduce pose interpreter networks for 6-DoF object pose
estimation. In contrast to other CNN-based approaches to pose estimation that
require expensively annotated object pose data, our pose interpreter network is
trained entirely on synthetic pose data. We use object masks as an intermediate
representation to bridge real and synthetic. We show that when combined with a
segmentation model trained on RGB images, our synthetically trained pose
interpreter network is able to generalize to real data. Our end-to-end system
for object pose estimation runs in real-time (20 Hz) on live RGB data, without
using depth information or ICP refinement.
