  We consider the problem of category-level 6D pose estimation from a single
RGB image. Our approach represents an object category as a cuboid mesh and
learns a generative model of the neural feature activations at each mesh vertex
to perform pose estimation through differentiable rendering. A common problem
of rendering-based approaches is that they rely on bounding box proposals,
which do not convey information about the 3D rotation of the object and are not
reliable when objects are partially occluded. Instead, we introduce a
coarse-to-fine optimization strategy that utilizes the rendering process to
estimate a sparse set of 6D object proposals, which are subsequently refined
with gradient-based optimization. The key to enabling the convergence of our
approach is a neural feature representation that is trained to be scale- and
rotation-invariant using contrastive learning. Our experiments demonstrate an
enhanced category-level 6D pose estimation performance compared to prior work,
particularly under strong partial occlusion.
