  Multi-view implicit scene reconstruction methods have become increasingly
popular due to their ability to represent complex scene details. Recent efforts
have been devoted to improving the representation of input information and to
reducing the number of views required to obtain high quality reconstructions.
Yet, perhaps surprisingly, the study of which views to select to maximally
improve scene understanding remains largely unexplored. We propose an
uncertainty-driven active vision approach for implicit scene reconstruction,
which leverages occupancy uncertainty accumulated across the scene using volume
rendering to select the next view to acquire. To this end, we develop an
occupancy-based reconstruction method which accurately represents scenes using
either 2D or 3D supervision. We evaluate our proposed approach on the ABC
dataset and the in the wild CO3D dataset, and show that: (1) we are able to
obtain high quality state-of-the-art occupancy reconstructions; (2) our
perspective conditioned uncertainty definition is effective to drive
improvements in next best view selection and outperforms strong baseline
approaches; and (3) we can further improve shape understanding by performing a
gradient-based search on the view selection candidates. Overall, our results
highlight the importance of view selection for implicit scene reconstruction,
making it a promising avenue to explore further.
