  Point cloud data from 3D LiDAR sensors are one of the most crucial sensor
modalities for versatile safety-critical applications such as self-driving
vehicles. Since the annotations of point cloud data is an expensive and
time-consuming process, therefore recently the utilisation of simulated
environments and 3D LiDAR sensors for this task started to get some popularity.
With simulated sensors and environments, the process for obtaining an annotated
synthetic point cloud data became much easier. However, the generated synthetic
point cloud data are still missing the artefacts usually exist in point cloud
data from real 3D LiDAR sensors. As a result, the performance of the trained
models on this data for perception tasks when tested on real point cloud data
is degraded due to the domain shift between simulated and real environments.
Thus, in this work, we are proposing a domain adaptation framework for bridging
this gap between synthetic and real point cloud data. Our proposed framework is
based on the deep cycle-consistent generative adversarial networks (CycleGAN)
architecture. We have evaluated the performance of our proposed framework on
the task of vehicle detection from a bird's eye view (BEV) point cloud images
coming from real 3D LiDAR sensors. The framework has shown competitive results
with an improvement of more than 7% in average precision score over other
baseline approaches when tested on real BEV point cloud images.
