  Most successful approaches to estimate the 6D pose of an object typically
train a neural network by supervising the learning with annotated poses in real
world images. These annotations are generally expensive to obtain and a common
workaround is to generate and train on synthetic scenes, with the drawback of
limited generalisation when the model is deployed in the real world. In this
work, a two-stage 6D object pose estimator framework that can be applied on top
of existing neural-network-based approaches and that does not require pose
annotations on real images is proposed. The first self-supervised stage
enforces the pose consistency between rendered predictions and real input
images, narrowing the gap between the two domains. The second stage fine-tunes
the previously trained model by enforcing the photometric consistency between
pairs of different object views, where one image is warped and aligned to match
the view of the other and thus enabling their comparison. In the absence of
both real image annotations and depth information, applying the proposed
framework on top of two recent approaches results in state-of-the-art
performance when compared to methods trained only on synthetic data, domain
adaptation baselines and a concurrent self-supervised approach on LINEMOD,
LINEMOD OCCLUSION and HomebrewedDB datasets.
