  A truly generalizable approach to rigid segmentation and motion estimation is
fundamental to 3D understanding of articulated objects and moving scenes. In
view of the closely intertwined relationship between segmentation and motion
estimates, we present an SE(3) equivariant architecture and a training strategy
to tackle this task in an unsupervised manner. Our architecture is composed of
two interconnected, lightweight heads. These heads predict segmentation masks
using point-level invariant features and estimate motion from SE(3) equivariant
features, all without the need for category information. Our training strategy
is unified and can be implemented online, which jointly optimizes the predicted
segmentation and motion by leveraging the interrelationships among scene flow,
segmentation mask, and rigid transformations. We conduct experiments on four
datasets to demonstrate the superiority of our method. The results show that
our method excels in both model performance and computational efficiency, with
only 0.25M parameters and 0.92G FLOPs. To the best of our knowledge, this is
the first work designed for category-agnostic part-level SE(3) equivariance in
dynamic point clouds.
