  Discovering the 3D atomic structure of molecules such as proteins and viruses
is a fundamental research problem in biology and medicine. Electron
Cryomicroscopy (Cryo-EM) is a promising vision-based technique for structure
estimation which attempts to reconstruct 3D structures from 2D images. This
paper addresses the challenging problem of 3D reconstruction from 2D Cryo-EM
images. A new framework for estimation is introduced which relies on modern
stochastic optimization techniques to scale to large datasets. We also
introduce a novel technique which reduces the cost of evaluating the objective
function during optimization by over five orders or magnitude. The net result
is an approach capable of estimating 3D molecular structure from large scale
datasets in about a day on a single workstation.
