The Next Best Underwater View

TL;DR

This paper introduces an optimization method for camera and lighting poses in scattering media like underwater environments, enhancing high-resolution imaging by maximizing information gain and utilizing prior 3D models.

Contribution

It generalizes the next best view concept to scattering media with movable lighting, optimizing platform movement for improved imaging quality.

Findings

Optimized camera/light poses increase image quality.

Utilizing prior 3D models improves scanning efficiency.

Method demonstrated in a scaled-down experimental setup.

Abstract

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.