The Next Best Underwater View
This work addresses imaging challenges in occlusion-prone underwater scenes, but it is incremental as it generalizes existing next best view concepts to scattering media and movable lighting.
The authors tackled the problem of imaging in scattering media like underwater by optimizing camera and light source poses to improve descattered recovery quality, demonstrating the approach in a scaled-down setup.
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.