  Judging the quality of samples synthesized by generative models can be
tedious and time consuming, especially for complex data structures, such as
point clouds. This paper presents a novel approach to quantify the realism of
local regions in LiDAR point clouds. Relevant features are learned from
real-world and synthetic point clouds by training on a proxy classification
task. Inspired by fair networks, we use an adversarial technique to discourage
the encoding of dataset-specific information. The resulting metric can assign a
quality score to samples without requiring any task specific annotations.
  In a series of experiments, we confirm the soundness of our metric by
applying it in controllable task setups and on unseen data. Additional
experiments show reliable interpolation capabilities of the metric between data
with varying degree of realism. As one important application, we demonstrate
how the local realism score can be used for anomaly detection in point clouds.
