发表机构
Stevens Institute of Technology（史蒂文斯理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出VCURF方法，通过测量虚拟相机渲染不一致性实现辐射场像素级不确定性量化，适用于NeRF和GS模型，并质疑现有视角选择策略。
AI 中文摘要
辐射场，无论是通过隐式（NeRF）还是显式（高斯泼溅）表示实现，都在以快速步伐推进新视角合成的最新技术水平。尽管它们生成的渲染视图通常引人注目，但并非没有错误。在本文中，我们提出了一种新的像素级不确定性量化方法，该方法基于测量辐射场模型在目标视点附近采样的虚拟相机中渲染结果之间的不一致性。我们将我们的方法命名为VCURF，即基于虚拟相机的辐射场不确定性。VCURF将辐射场模型视为黑盒，仅假设它能够按需渲染颜色和深度。这一特性使我们的方法无需任何修改即可适用于NeRF和GS模型。我们在数据集、辐射场模型和基线的组合上进行的实验证明了VCURF在像素级不确定性估计中的有效性。我们以质疑当前大多数文献处理视角选择方式的发现作为论文的结论。
英文摘要
Radiance fields, implemented with either implicit (NeRF) or explicit (Gaussian Splatting) representations, are advancing the state of the art in novel view synthesis at a rapid pace. Even though the rendered views they generate are often compelling, they are not free of errors. In this paper, we propose a new approach for pixel-wise uncertainty quantification based on measuring the inconsistencies among renderings by the radiance field model in virtual cameras sampled near the target viewpoint. We named our approach VCURF for Virtual Camera-based Uncertainty of Radiance Fields. VCURF treats the radiance field model as a black box, only assuming that it is capable of rendering color and depth on demand. This property makes our approach applicable to both NeRF and GS models without any modification. Our experiments on a combination of datasets, radiance field models and baselines demonstrate VCURF's effectiveness in pixel-wise uncertainty estimation. We conclude the paper with findings that question the way view selection is tackled by the majority of the current literature.
