# The Next Best Underwater View

arXiv: https://arxiv.org/abs/1512.01789 · HTML: https://mlsanity.com/paper/1512.01789/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Dec 2015.

## Built on by (most cited first, 4 of 4)

- [Learned, uncertainty-driven adaptive acquisition for photon-efficient scanning microscopy](https://mlsanity.com/paper/2310.16102/) (2023)
- [A deep learning approach for marine snow synthesis and removal](https://mlsanity.com/paper/2311.15584/) (2023)
- [Feature Space Exploration For Planning Initial Benthic AUV Surveys](https://mlsanity.com/paper/2105.11598/) (2021)
- [Learning Underwater Active Perception in Simulation](https://mlsanity.com/paper/2504.17817/) (2025)

## Abstract (arXiv)

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.