  Autonomous robot manipulation involves estimating the translation and
orientation of the object to be manipulated as a 6-degree-of-freedom (6D) pose.
Methods using RGB-D data have shown great success in solving this problem.
However, there are situations where cost constraints or the working environment
may limit the use of RGB-D sensors. When limited to monocular camera data only,
the problem of object pose estimation is very challenging. In this work, we
introduce a novel method called SilhoNet that predicts 6D object pose from
monocular images. We use a Convolutional Neural Network (CNN) pipeline that
takes in Region of Interest (ROI) proposals to simultaneously predict an
intermediate silhouette representation for objects with an associated occlusion
mask and a 3D translation vector. The 3D orientation is then regressed from the
predicted silhouettes. We show that our method achieves better overall
performance on the YCB-Video dataset than two state-of-the art networks for 6D
pose estimation from monocular image input.
