  Continual Learning aims to learn multiple incoming new tasks continually, and
to keep the performance of learned tasks at a consistent level. However,
existing research on continual learning assumes the pose of the object is
pre-defined and well-aligned. For practical application, this work focuses on
pose-agnostic continual learning tasks, where the object's pose changes
dynamically and unpredictably. The point cloud augmentation adopted from past
approaches would sharply rise with the task increment in the continual learning
process. To address this problem, we inject the equivariance as the additional
prior knowledge into the networks. We proposed a novel continual learning model
that effectively distillates previous tasks' geometric equivariance
information. The experiments show that our method overcomes the challenge of
pose-agnostic scenarios in several mainstream point cloud datasets. We further
conduct ablation studies to evaluate the validation of each component of our
approach.
