  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.
