  Detecting pedestrians is a crucial task in autonomous driving systems to
ensure the safety of drivers and pedestrians. The technologies involved in
these algorithms must be precise and reliable, regardless of environment
conditions. Relying solely on RGB cameras may not be enough to recognize road
environments in situations where cameras cannot capture scenes properly. Some
approaches aim to compensate for these limitations by combining RGB cameras
with TOF sensors, such as LIDARs. However, there are few works that address
this problem using exclusively the 3D geometric information provided by LIDARs.
In this paper, we propose a PointNet++ based architecture to detect pedestrians
in dense 3D point clouds. The aim is to explore the potential contribution of
geometric information alone in pedestrian detection systems. We also present a
semi-automatic labeling system that transfers pedestrian and non-pedestrian
labels from RGB images onto the 3D domain. The fact that our datasets have RGB
registered with point clouds enables label transferring by back projection from
2D bounding boxes to point clouds, with only a light manual supervision to
validate results. We train PointNet++ with the geometry of the resulting 3D
labelled clusters. The evaluation confirms the effectiveness of the proposed
method, yielding precision and recall values around 98%.
