  We perform detailed theoretical analysis of an expectation-maximization-based
algorithm recently proposed in for solving a variation of the 3D registration
problem, named multi-model 3D registration. Despite having shown superior
empirical results, did not theoretically justify the conditions under which the
EM approach converges to the ground truth. In this project, we aim to close
this gap by establishing such conditions. In particular, the analysis revolves
around the usage of probabilistic tail bounds that are developed and applied in
various instances throughout the course. The problem studied in this project
stands as another example, different from those seen in the course, in which
tail-bounds help advance our algorithmic understanding in a probabilistic way.
We provide self-contained background materials on 3D Registration
