To image in high resolution large and occlusion-prone
scenes, a camera must move above and around. Degradation
of visibility due to geometric occlusions and distances
is exacerbated by scattering, when the scene is in a
participating medium. Moreover, underwater and in other
media, artificial lighting is needed. Overall, data quality
depends on the observed surface, medium and the time varying
poses of the camera and light source (C&L). This
work proposes to optimize C&L poses as they move, so that
the surface is scanned efficiently and the descattered recovery
has the highest quality. The work generalizes the next
best view concept of robot vision to scattering media and
cooperative movable lighting. It also extends descattering
to platforms that move optimally. The optimization criterion
is information gain, taken from information theory. We
exploit the existence of a prior rough 3D model, since underwater
such a model is routinely obtained using sonar.
We demonstrate this principle in a scaled-down setup.