  Category-level object pose estimation aims to find 6D object poses of
previously unseen object instances from known categories without access to
object CAD models. To reduce the huge amount of pose annotations needed for
category-level learning, we propose for the first time a self-supervised
learning framework to estimate category-level 6D object pose from single 3D
point clouds.During training, our method assumes no ground-truth pose
annotations, no CAD models, and no multi-view supervision. The key to our
method is to disentangle shape and pose through an invariant shape
reconstruction module and an equivariant pose estimation module, empowered by
SE(3) equivariant point cloud networks.The invariant shape reconstruction
module learns to perform aligned reconstructions, yielding a category-level
reference frame without using any annotations. In addition, the equivariant
pose estimation module achieves category-level pose estimation accuracy that is
comparable to some fully supervised methods. Extensive experiments demonstrate
the effectiveness of our approach on both complete and partial depth point
clouds from the ModelNet40 benchmark, and on real depth point clouds from the
NOCS-REAL 275 dataset. The project page with code and visualizations can be
found at: https://dragonlong.github.io/equi-pose.
