  Dynamic reconstruction and spatiotemporal novel-view synthesis of non-rigidly
deforming scenes recently gained increased attention. While existing work
achieves impressive quality and performance on multi-view or teleporting camera
setups, most methods fail to efficiently and faithfully recover motion and
appearance from casual monocular captures. This paper contributes to the field
by introducing a new method for dynamic novel view synthesis from monocular
video, such as casual smartphone captures.
  Our approach represents the scene as a $\textit{dynamic neural point cloud}$,
an implicit time-conditioned point distribution that encodes local geometry and
appearance in separate hash-encoded neural feature grids for static and dynamic
regions. By sampling a discrete point cloud from our model, we can efficiently
render high-quality novel views using a fast differentiable rasterizer and
neural rendering network. Similar to recent work, we leverage advances in
neural scene analysis by incorporating data-driven priors like monocular depth
estimation and object segmentation to resolve motion and depth ambiguities
originating from the monocular captures. In addition to guiding the
optimization process, we show that these priors can be exploited to explicitly
initialize our scene representation to drastically improve optimization speed
and final image quality. As evidenced by our experimental evaluation, our
dynamic point cloud model not only enables fast optimization and real-time
frame rates for interactive applications, but also achieves competitive image
quality on monocular benchmark sequences.
  Our project page is available at
https://moritzkappel.github.io/projects/dnpc.
