  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. This work
proposes to optimize camera/light 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.
