  Object detection on Lidar point cloud data is a promising technology for
autonomous driving and robotics which has seen a significant rise in
performance and accuracy during recent years. Particularly uncertainty
estimation is a crucial component for down-stream tasks and deep neural
networks remain error-prone even for predictions with high confidence.
Previously proposed methods for quantifying prediction uncertainty tend to
alter the training scheme of the detector or rely on prediction sampling which
results in vastly increased inference time. In order to address these two
issues, we propose LidarMetaDetect (LMD), a light-weight post-processing scheme
for prediction quality estimation. Our method can easily be added to any
pre-trained Lidar object detector without altering anything about the base
model and is purely based on post-processing, therefore, only leading to a
negligible computational overhead. Our experiments show a significant increase
of statistical reliability in separating true from false predictions. We
propose and evaluate an additional application of our method leading to the
detection of annotation errors. Explicit samples and a conservative count of
annotation error proposals indicates the viability of our method for
large-scale datasets like KITTI and nuScenes. On the widely-used nuScenes test
dataset, 43 out of the top 100 proposals of our method indicate, in fact,
erroneous annotations.
