通过渐进式相机布置提升复杂环境中无限制导航的NeRF质量
Improving NeRF Quality by Progressive Camera Placement for Unrestricted Navigation in Complex Environments
AI总结：
本文提出一种渐进式相机布置算法，通过优化数据采集提升复杂环境中NeRF自由视点导航的重建质量，适用于任意NeRF模型且优于现有方法。
摘要：
神经辐射场（Neural Radiance Fields，简称NeRFs）极大地提升了新视角合成和用于渲染的三维重建。NeRFs在物体中心的重建上取得了令人印象深刻的结果，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。虽然算法改进对最终新视角合成的质量起着重要作用，但在这项工作中，我们表明，由于优化NeRF本质上是一个数据驱动过程，高质量数据在重建的最终质量中扮演着基础性角色。因此，选择数据样本——在此情境下即相机——的方式至关重要，需确保优化最终能收敛到支持高质量自由视点导航的解。我们的主要贡献是一种算法，该算法能高效地提出新的相机布置，以在最小假设下提升视觉质量。我们的解决方案可与任何NeRF模型配合使用，并且优于基线和类似工作。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
