  We present a technique for visualizing point clouds using a neural network.
Our technique allows for an instant preview of any point cloud, and bypasses
the notoriously difficult surface reconstruction problem or the need to
estimate oriented normals for splat-based rendering. We cast the preview
problem as a conditional image-to-image translation task, and design a neural
network that translates point depth-map directly into an image, where the point
cloud is visualized as though a surface was reconstructed from it. Furthermore,
the resulting appearance of the visualized point cloud can be, optionally,
conditioned on simple control variables (e.g., color and light). We demonstrate
that our technique instantly produces plausible images, and can, on-the-fly
effectively handle noise, non-uniform sampling, and thin surfaces sheets.
