  The performance of object detection methods based on LiDAR information is
heavily impacted by the availability of training data, usually limited to
certain laser devices. As a result, the use of synthetic data is becoming
popular when training neural network models, as both sensor specifications and
driving scenarios can be generated ad-hoc. However, bridging the gap between
virtual and real environments is still an open challenge, as current simulators
cannot completely mimic real LiDAR operation. To tackle this issue, domain
adaptation strategies are usually applied, obtaining remarkable results on
vehicle detection when applied to range view (RV) and bird's eye view (BEV)
projections while failing for smaller road agents. In this paper, we present a
BEV domain adaptation method based on CycleGAN that uses prior semantic
classification in order to preserve the information of small objects of
interest during the domain adaptation process. The quality of the generated
BEVs has been evaluated using a state-of-the-art 3D object detection framework
at KITTI 3D Object Detection Benchmark. The obtained results show the
advantages of the proposed method over the existing alternatives.
