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.

本文提出了一种无监督领域自适应方法（UDA-COPE）来实现基于类别的物体姿态估计，不需要使用目标域的姿态标签，而是使用一种带有学生 - 教师自监督学习框架的姿态估计网络进行训练，并在预测出来的归一化物体坐标空间地图和真实点云之间介绍了一个双向过滤器来提供给学生网络训练更可靠的伪标签，实验结果表明，该算法在不使用目标域 GT 标签的情况下，可以达到与基于 GT 标签的现有方法相当甚至更好的表现。