  Partial point cloud registration is a challenging problem in robotics,
especially when the robot undergoes a large transformation, causing a
significant initial pose error and a low overlap between measurements. This
work proposes exploiting equivariant learning from 3D point clouds to improve
registration robustness. We propose SE3ET, an SE(3)-equivariant registration
framework that employs equivariant point convolution and equivariant
transformer designs to learn expressive and robust geometric features. We
tested the proposed registration method on indoor and outdoor benchmarks where
the point clouds are under arbitrary transformations and low overlapping
ratios. We also provide generalization tests and run-time performance.
