  Object detection is an important task in environment perception for
autonomous driving. Modern 2D object detection frameworks such as Yolo, SSD or
Faster R-CNN predict multiple bounding boxes per object that are refined using
Non-Maximum-Suppression (NMS) to suppress all but one bounding box. While
object detection itself is fully end-to-end learnable and does not require any
manual parameter selection, standard NMS is parametrized by an overlap
threshold that has to be chosen by hand. In practice, this often leads to an
inability of standard NMS strategies to distinguish different objects in
crowded scenes in the presence of high mutual occlusion, e.g. for parked cars
or crowds of pedestrians. Our novel Visibility Guided NMS (vg-NMS) leverages
both pixel-based as well as amodal object detection paradigms and improves the
detection performance especially for highly occluded objects with little
computational overhead. We evaluate vg-NMS using KITTI, VIPER as well as the
Synscapes dataset and show that it outperforms current state-of-the-art NMS.
