  LiDAR object detection algorithms based on neural networks for autonomous
driving require large amounts of data for training, validation, and testing. As
real-world data collection and labeling are time-consuming and expensive,
simulation-based synthetic data generation is a viable alternative. However,
using simulated data for the training of neural networks leads to a domain
shift of training and testing data due to differences in scenes, scenarios, and
distributions. In this work, we quantify the sim-to-real domain shift by means
of LiDAR object detectors trained with a new scenario-identical real-world and
simulated dataset. In addition, we answer the questions of how well the
simulated data resembles the real-world data and how well object detectors
trained on simulated data perform on real-world data. Further, we analyze point
clouds at the target-level by comparing real-world and simulated point clouds
within the 3D bounding boxes of the targets. Our experiments show that a
significant sim-to-real domain shift exists even for our scenario-identical
datasets. This domain shift amounts to an average precision reduction of around
14 % for object detectors trained with simulated data. Additional experiments
reveal that this domain shift can be lowered by introducing a simple noise
model in simulation. We further show that a simple downsampling method to model
real-world physics does not influence the performance of the object detectors.
