A Realism Metric for Generated LiDAR Point Clouds

A considerable amount of research is concerned with the generation of realistic sensor data.
LiDAR point clouds are generated by complex simulations or learned generative models. The
generated data is usually exploited to enable or improve downstream perception algorithms.
Two major questions arise from these procedures: First, how to evaluate the realism of the
generated data? Second, does more realistic data also lead to better perception performance?
This paper addresses both questions and presents a novel metric to quantify the realism of
LiDAR point clouds. Relevant features are learned from real-world and synthetic point clouds
by training on a proxy classification task. In a series of experiments, we demonstrate the
application of our metric to determine the realism of generated LiDAR data and compare
the realism estimation of our metric to the performance of a segmentation model. We con-
firm that our metric provides an indication for the downstream segmentation performance.