  We characterize the problem of pose estimation for rigid objects in terms of
determining viewpoint to explain coarse pose and keypoint prediction to capture
the finer details. We address both these tasks in two different settings - the
constrained setting with known bounding boxes and the more challenging
detection setting where the aim is to simultaneously detect and correctly
estimate pose of objects. We present Convolutional Neural Network based
architectures for these and demonstrate that leveraging viewpoint estimates can
substantially improve local appearance based keypoint predictions. In addition
to achieving significant improvements over state-of-the-art in the above tasks,
we analyze the error modes and effect of object characteristics on performance
to guide future efforts towards this goal.
