  In this work, we tackle the challenging problem of category-level object pose
and size estimation from a single depth image. Although previous
fully-supervised works have demonstrated promising performance, collecting
ground-truth pose labels is generally time-consuming and labor-intensive.
Instead, we propose a label-free method that learns to enforce the geometric
consistency between category template mesh and observed object point cloud
under a self-supervision manner. Specifically, our method consists of three key
components: differentiable shape deformation, registration, and rendering. In
particular, shape deformation and registration are applied to the template mesh
to eliminate the differences in shape, pose and scale. A differentiable
renderer is then deployed to enforce geometric consistency between point clouds
lifted from the rendered depth and the observed scene for self-supervision. We
evaluate our approach on real-world datasets and find that our approach
outperforms the simple traditional baseline by large margins while being
competitive with some fully-supervised approaches.
