Category-level articulated object pose estimation aims to estimate a
hierarchy of articulation-aware object poses of an unseen articulated object
from a known category. To reduce the heavy annotations needed for supervised
learning methods, we present a novel self-supervised strategy that solves this
problem without any human labels. Our key idea is to factorize canonical shapes
and articulated object poses from input articulated shapes through part-level
equivariant shape analysis. Specifically, we first introduce the concept of
part-level SE(3) equivariance and devise a network to learn features of such
property. Then, through a carefully designed fine-grained pose-shape
disentanglement strategy, we expect that canonical spaces to support pose
estimation could be induced automatically. Thus, we could further predict
articulated object poses as per-part rigid transformations describing how parts
transform from their canonical part spaces to the camera space. Extensive
experiments demonstrate the effectiveness of our method on both complete and
partial point clouds from synthetic and real articulated object datasets.

这篇论文提出了一种基于自监督学习的方法，通过部分等变形状分析提取规范形状和物体姿态，再通过刚性变换描述部件从规范空间到相机空间的转换，实现未知关节感知物体的层次化姿态估计。