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.

翻译：神经辐射场（NeRF）显著提升了新视角合成和三维重建的渲染质量。尽管NeRF在中心物体重建中取得了令人瞩目的成果，但在复杂环境（如房间、房屋等）中进行自由视点导航时，新视角合成质量往往存在问题。虽然算法改进对新视角合成质量至关重要，但本研究表明，由于NeRF优化本质上是一个数据驱动过程，高质量数据对最终重建质量起着基础性作用。因此，关键挑战在于如何选择数据样本（即相机位姿），使优化收敛到能实现高质量自由视点导航的解决方案。本文主要贡献在于提出一种算法，能以最小假设高效推荐新的相机放置方案，从而提升视觉质量。该解决方案兼容任何NeRF模型，且性能优于现有基线和同类工作。