  We study the problem of aligning two sets of 3D geometric primitives given
known correspondences. Our first contribution is to show that this primitive
alignment framework unifies five perception problems including point cloud
registration, primitive (mesh) registration, category-level 3D registration,
absolution pose estimation (APE), and category-level APE. Our second
contribution is to propose DynAMical Pose estimation (DAMP), the first general
and practical algorithm to solve primitive alignment problem by simulating
rigid body dynamics arising from virtual springs and damping, where the springs
span the shortest distances between corresponding primitives. We evaluate DAMP
in simulated and real datasets across all five problems, and demonstrate (i)
DAMP always converges to the globally optimal solution in the first three
problems with 3D-3D correspondences; (ii) although DAMP sometimes converges to
suboptimal solutions in the last two problems with 2D-3D correspondences, using
a scheme for escaping local minima, DAMP always succeeds. Our third
contribution is to demystify the surprising empirical performance of DAMP and
formally prove a global convergence result in the case of point cloud
registration by charactering local stability of the equilibrium points of the
underlying dynamical system.
