  High energy density physics (HEDP) experiments commonly involve a dynamic
wave-front propagating inside a low-density foam. This effect affects its
density and hence, its transparency. A common problem in foam production is the
creation of defective foams. Accurate information on their dimension and
homogeneity is required to classify the foams' quality. Therefore, those
parameters are being characterized using a 3D-measuring laser confocal
microscope. For each foam, five images are taken: two 2D images representing
the top and bottom surface foam planes and three images of side cross-sections
from 3D scannings. An expert has to do the complicated, harsh, and exhausting
work of manually classifying the foam's quality through the image set and only
then determine whether the foam can be used in experiments or not. Currently,
quality has two binary levels of normal vs. defective. At the same time,
experts are commonly required to classify a sub-class of normal-defective,
i.e., foams that are defective but might be sufficient for the needed
experiment. This sub-class is problematic due to inconclusive judgment that is
primarily intuitive. In this work, we present a novel state-of-the-art
multi-view deep learning classification model that mimics the physicist's
perspective by automatically determining the foams' quality classification and
thus aids the expert. Our model achieved 86\% accuracy on upper and lower
surface foam planes and 82\% on the entire set, suggesting interesting
heuristics to the problem. A significant added value in this work is the
ability to regress the foam quality instead of binary deduction and even
explain the decision visually. The source code used in this work, as well as
other relevant sources, are available at:
https://github.com/Scientific-Computing-Lab-NRCN/Multi-View-Foams.git
