Casually captured Neural Radiance Fields (NeRFs) suffer from artifacts such
as floaters or flawed geometry when rendered outside the camera trajectory.
Existing evaluation protocols often do not capture these effects, since they
usually only assess image quality at every 8th frame of the training capture.
To push forward progress in novel-view synthesis, we propose a new dataset and
evaluation procedure, where two camera trajectories are recorded of the scene:
one used for training, and the other for evaluation. In this more challenging
in-the-wild setting, we find that existing hand-crafted regularizers do not
remove floaters nor improve scene geometry. Thus, we propose a 3D
diffusion-based method that leverages local 3D priors and a novel density-based
score distillation sampling loss to discourage artifacts during NeRF
optimization. We show that this data-driven prior removes floaters and improves
scene geometry for casual captures.

该研究提出了一种新的数据集和评估程序，利用 3D 扩散方法和基于密度的分数蒸馏采样损失来避免 NeRF 优化过程中的呈现伪像和错误几何图形问题。使用这种数据驱动的先验方法可以去除浮动物并改善场景几何结构。