Occluded object reconstruction for first responders with augmented reality glasses using conditional generative adversarial networks
This addresses safety risks for first responders by enabling better visibility of occluded objects, though it appears incremental as it applies existing deep learning methods to a specific domain.
The paper tackled the problem of reconstructing partially occluded hazardous objects for firefighters using conditional generative adversarial networks, resulting in a system that superimposes reconstructed images on input to provide transparency.
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.