  Object pose estimation from a single RGB image is a challenging problem due
to variable lighting conditions and viewpoint changes. The most accurate pose
estimation networks implement pose refinement via reprojection of a known,
textured 3D model, however, such methods cannot be applied without high quality
3D models of the observed objects. In this work we propose an approach, namely
an Innovation CNN, to object pose estimation refinement that overcomes the
requirement for reprojecting a textured 3D model. Our approach improves initial
pose estimation progressively by applying the Innovation CNN iteratively in a
stochastic gradient descent (SGD) framework. We evaluate our method on the
popular LINEMOD and Occlusion LINEMOD datasets and obtain state-of-the-art
performance on both datasets.
