  We have developed a convolutional neural network (CNN) to reconstruct the
shape of irregular rough particles from their interferometric images. The CNN
is based on a UNET architecture with residual block modules. The database has
been constructed using the experimental patterns generated by perfectly known
pseudo-particles programmed on a Digital Micromirror Device (DMD) and under
laser illumination. The CNN has been trained on a basis of 18000 experimental
interferometric images using the AUSTRAL super computer (at CRIANN in
Normandy). The CNN is tested in the case of centrosymmetric (stick, cross,
dendrite) and non-centrosymmetric (like T, Y or L) particles. The size and the
3D orientation of the programmed particles are random. The different shapes are
reconstructed by the CNN with good accuracy. Using three angles of view, the 3D
reconstruction of particles from three reconstructed faces can be further done.
