  The central problem in cryo-electron microscopy (cryo-EM) is to recover the
3D structure from noisy 2D projection images which requires estimating the
missing projection angles (poses). Recent methods attempted to solve the 3D
reconstruction problem with the autoencoder architecture, which suffers from
the latent vector space sampling problem and frequently produces suboptimal
pose inferences and inferior 3D reconstructions. Here we present an improved
autoencoder architecture called ACE (Asymmetric Complementary autoEncoder),
based on which we designed the ACE-EM method for cryo-EM 3D reconstructions.
Compared to previous methods, ACE-EM reached higher pose space coverage within
the same training time and boosted the reconstruction performance regardless of
the choice of decoders. With this method, the Nyquist resolution (highest
possible resolution) was reached for 3D reconstructions of both simulated and
experimental cryo-EM datasets. Furthermore, ACE-EM is the only amortized
inference method that reached the Nyquist resolution.
