  Widely-used LiDAR-based 3D object detectors often neglect fundamental
geometric information readily available from the object proposals in their
confidence estimation. This is mostly due to architectural design choices,
which were often adopted from the 2D image domain, where geometric context is
rarely available. In 3D, however, considering the object properties and its
surroundings in a holistic way is important to distinguish between true and
false positive detections, e.g. occluded pedestrians in a group. To address
this, we present GACE, an intuitive and highly efficient method to improve the
confidence estimation of a given black-box 3D object detector. We aggregate
geometric cues of detections and their spatial relationships, which enables us
to properly assess their plausibility and consequently, improve the confidence
estimation. This leads to consistent performance gains over a variety of
state-of-the-art detectors. Across all evaluated detectors, GACE proves to be
especially beneficial for the vulnerable road user classes, i.e. pedestrians
and cyclists.
