Fourier PlenOctrees have shown to be an efficient representation for
real-time rendering of dynamic Neural Radiance Fields (NeRF). Despite its many
advantages, this method suffers from artifacts introduced by the involved
compression when combining it with recent state-of-the-art techniques for
training the static per-frame NeRF models. In this paper, we perform an
in-depth analysis of these artifacts and leverage the resulting insights to
propose an improved representation. In particular, we present a novel density
encoding that adapts the Fourier-based compression to the characteristics of
the transfer function used by the underlying volume rendering procedure and
leads to a substantial reduction of artifacts in the dynamic model.
Furthermore, we show an augmentation of the training data that relaxes the
periodicity assumption of the compression. We demonstrate the effectiveness of
our enhanced Fourier PlenOctrees in the scope of quantitative and qualitative
evaluations on synthetic and real-world scenes.

通过对动态模型中的压缩引入的伪影进行深入分析，并利用所得的见解提出一种改进的表示方法，特别是提出了一种根据基于 Fourier 的压缩来适应底层体渲染过程的传递函数特征的新密度编码，从而显著减少动态模型中的伪影。此外，通过对训练数据的扩充来放松压缩的周期性假设。在合成和真实场景的定量和定性评估范围内展示了增强的 Fourier PlenOctrees 的有效性。