  6D object pose estimation is a prerequisite for many applications. In recent
years, monocular pose estimation has attracted much research interest because
it does not need depth measurements. In this work, we introduce ConvPoseCNN, a
fully convolutional architecture that avoids cutting out individual objects.
Instead we propose pixel-wise, dense prediction of both translation and
orientation components of the object pose, where the dense orientation is
represented in Quaternion form. We present different approaches for aggregation
of the dense orientation predictions, including averaging and clustering
schemes. We evaluate ConvPoseCNN on the challenging YCB-Video Dataset, where we
show that the approach has far fewer parameters and trains faster than
comparable methods without sacrificing accuracy. Furthermore, our results
indicate that the dense orientation prediction implicitly learns to attend to
trustworthy, occlusion-free, and feature-rich object regions.
