4DSR-GCN: 4D Video Point Cloud Upsampling using Graph Convolutional
  Networks
Lorenzo Berlincioni Stefano Berretti Marco Bertini Alberto Del Bimbo
Time varying sequences of 3D point clouds, or 4D point clouds, are now being
acquired at an increasing pace in several applications (e.g., LiDAR in
autonomous or assisted driving). In many cases, such volume of data is
transmitted, thus requiring that proper compression tools are applied to either
reduce the resolution or the bandwidth. In this paper, we propose a new
solution for upscaling and restoration of time-varying 3D video point clouds
after they have been heavily compressed. In consideration of recent growing
relevance of 3D applications, %We focused on a model allowing user-side
upscaling and artifact removal for 3D video point clouds, a real-time stream of
which would require . Our model consists of a specifically designed Graph
Convolutional Network (GCN) that combines Dynamic Edge Convolution and Graph
Attention Networks for feature aggregation in a Generative Adversarial setting.
By taking inspiration PointNet++, We present a different way to sample dense
point clouds with the intent to make these modules work in synergy to provide
each node enough features about its neighbourhood in order to later on generate
new vertices. Compared to other solutions in the literature that address the
same task, our proposed model is capable of obtaining comparable results in
terms of quality of the reconstruction, while using a substantially lower
number of parameters (about 300KB), making our solution deployable in edge
computing devices such as LiDAR.