  We propose and evaluate a neural point-based graphics method that can model
semi-transparent scene parts. Similarly to its predecessor pipeline, ours uses
point clouds to model proxy geometry, and augments each point with a neural
descriptor. Additionally, a learnable transparency value is introduced in our
approach for each point.
  Our neural rendering procedure consists of two steps. Firstly, the point
cloud is rasterized using ray grouping into a multi-channel image. This is
followed by the neural rendering step that "translates" the rasterized image
into an RGB output using a learnable convolutional network. New scenes can be
modeled using gradient-based optimization of neural descriptors and of the
rendering network.
  We show that novel views of semi-transparent point cloud scenes can be
generated after training with our approach. Our experiments demonstrate the
benefit of introducing semi-transparency into the neural point-based modeling
for a range of scenes with semi-transparent parts.
