  Neural Radiance Fields, or NeRFs, have drastically improved novel view
synthesis and 3D reconstruction for rendering. NeRFs achieve impressive results
on object-centric reconstructions, but the quality of novel view synthesis with
free-viewpoint navigation in complex environments (rooms, houses, etc) is often
problematic. While algorithmic improvements play an important role in the
resulting quality of novel view synthesis, in this work, we show that because
optimizing a NeRF is inherently a data-driven process, good quality data play a
fundamental role in the final quality of the reconstruction. As a consequence,
it is critical to choose the data samples -- in this case the cameras -- in a
way that will eventually allow the optimization to converge to a solution that
allows free-viewpoint navigation with good quality. Our main contribution is an
algorithm that efficiently proposes new camera placements that improve visual
quality with minimal assumptions. Our solution can be used with any NeRF model
and outperforms baselines and similar work.
