  We present a novel post-processing tool for semantic segmentation of LiDAR
point cloud data, called LidarMetaSeg, which estimates the prediction quality
segmentwise. For this purpose we compute dispersion measures based on network
probability outputs as well as feature measures based on point cloud input
features and aggregate them on segment level. These aggregated measures are
used to train a meta classification model to predict whether a predicted
segment is a false positive or not and a meta regression model to predict the
segmentwise intersection over union. Both models can then be applied to
semantic segmentation inferences without knowing the ground truth. In our
experiments we use different LiDAR segmentation models and datasets and analyze
the power of our method. We show that our results outperform other standard
approaches.
