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）显著提升了渲染中的新视角合成与三维重建质量。虽然NeRF在物体中心的重建任务上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，新视角合成的质量往往存在问题。尽管算法改进对新视角合成的最终质量至关重要，但本研究表明：由于NeRF优化本质上是一个数据驱动过程，优质数据在重建最终质量中起着根本性作用。因此，关键问题在于如何选择数据样本（即相机位姿），使其能够引导优化过程收敛至支持高质量自由视点导航的解。本文的核心贡献是一种高效提出新相机布置方案的算法，该算法以最低假设条件提升视觉质量。该方案可适配任意NeRF模型，且性能优于基线方法与同类工作。