  We introduce a new method for category-level pose estimation which produces a
distribution over predicted poses by integrating 3D shape estimates from a
generative object model with segmentation information. Given an input
depth-image of an object, our variable-time method uses a mixture density
network architecture to produce a multi-modal distribution over 3DOF poses;
this distribution is then combined with a prior probability encouraging
silhouette agreement between the observed input and predicted object pose. Our
approach significantly outperforms the current state-of-the-art in
category-level 3DOF pose estimation---which outputs a point estimate and does
not explicitly incorporate shape and segmentation information---as measured on
the Pix3D and ShapeNet datasets.
