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.

优化 NeRF 模型时，良好的数据样本和有效的相机位置选择对于最终重建的可视化质量至关重要。本研究提出了一种算法，能够以最少的假设来提供改善可视化质量的新相机位置，并在各方面优于基线和类似方法。