Redefining Recon: Bridging Gaps with UAVs, 360 degree Cameras, and
  Neural Radiance Fields
Hartmut Surmann Niklas Digakis Jan-Nicklas Kremer Julien Meine Max Schulte Niklas Voigt
In the realm of digital situational awareness during disaster situations,
accurate digital representations, like 3D models, play an indispensable role.
To ensure the safety of rescue teams, robotic platforms are often deployed to
generate these models. In this paper, we introduce an innovative approach that
synergizes the capabilities of compact Unmaned Arial Vehicles (UAVs), smaller
than 30 cm, equipped with 360 degree cameras and the advances of Neural
Radiance Fields (NeRFs). A NeRF, a specialized neural network, can deduce a 3D
representation of any scene using 2D images and then synthesize it from various
angles upon request. This method is especially tailored for urban environments
which have experienced significant destruction, where the structural integrity
of buildings is compromised to the point of barring entry-commonly observed
post-earthquakes and after severe fires. We have tested our approach through
recent post-fire scenario, underlining the efficacy of NeRFs even in
challenging outdoor environments characterized by water, snow, varying light
conditions, and reflective surfaces.