http://graphics.stanford.edu/papers/fasticp/fasticp_paper.pdf
„The ICP (Iterative Closest Point) algorithm is widely used for geometric alignment of three-dimensional models when an initial
estimate of the relative pose is known. Many variants of ICP have
been proposed, affecting all phases of the algorithm from the selection and matching of points to the minimization strategy. We
enumerate and classify many of these variants, and evaluate their
effect on the speed with which the correct alignment is reached.
In order to improve convergence for nearly-flat meshes with small
features, such as inscribed surfaces, we introduce a new variant
based on uniform sampling of the space of normals. We conclude
by proposing a combination of ICP variants optimized for high
speed. We demonstrate an implementation that is able to align
two range images in a few tens of milliseconds, assuming a good
initial guess. This capability has potential application to real-time
3D model acquisition and model-based tracking.“
http://graphics.stanford.edu/papers/fasticp/fasticp_paper.pdf