  Firefighters suffer a variety of life-threatening risks, including
line-of-duty deaths, injuries, and exposures to hazardous substances. Support
for reducing these risks is important. We built a partially occluded object
reconstruction method on augmented reality glasses for first responders. We
used a deep learning based on conditional generative adversarial networks to
train associations between the various images of flammable and hazardous
objects and their partially occluded counterparts. Our system then
reconstructed an image of a new flammable object. Finally, the reconstructed
image was superimposed on the input image to provide "transparency". The system
imitates human learning about the laws of physics through experience by
learning the shape of flammable objects and the flame characteristics.
