  Learning to estimate object pose often requires ground-truth (GT) labels,
such as CAD model and absolute-scale object pose, which is expensive and
laborious to obtain in the real world. To tackle this problem, we propose an
unsupervised domain adaptation (UDA) for category-level object pose estimation,
called UDA-COPE. Inspired by recent multi-modal UDA techniques, the proposed
method exploits a teacher-student self-supervised learning scheme to train a
pose estimation network without using target domain pose labels. We also
introduce a bidirectional filtering method between the predicted normalized
object coordinate space (NOCS) map and observed point cloud, to not only make
our teacher network more robust to the target domain but also to provide more
reliable pseudo labels for the student network training. Extensive experimental
results demonstrate the effectiveness of our proposed method both
quantitatively and qualitatively. Notably, without leveraging target-domain GT
labels, our proposed method achieved comparable or sometimes superior
performance to existing methods that depend on the GT labels.
