  Neural Radiance Fields (NeRF) have quickly become the primary approach for 3D
reconstruction and novel view synthesis in recent years due to their remarkable
performance. Despite the huge interest in NeRF methods, a practical use case of
NeRFs has largely been ignored; the exploration of the scene space modelled by
a NeRF. In this paper, for the first time in the literature, we propose and
formally define the scene exploration framework as the efficient discovery of
NeRF model inputs (i.e. coordinates and viewing angles), using which one can
render novel views that adhere to user-selected criteria. To remedy the lack of
approaches addressing scene exploration, we first propose two baseline methods
called Guided-Random Search (GRS) and Pose Interpolation-based Search (PIBS).
We then cast scene exploration as an optimization problem, and propose the
criteria-agnostic Evolution-Guided Pose Search (EGPS) for efficient
exploration. We test all three approaches with various criteria (e.g. saliency
maximization, image quality maximization, photo-composition quality
improvement) and show that our EGPS performs more favourably than other
baselines. We finally highlight key points and limitations, and outline
directions for future research in scene exploration.
