On the Calibration of Uncertainty Estimation in LiDAR-based Semantic
  Segmentation
Mariella Dreissig Florian Piewak Joschka Boedecker
The confidence calibration of deep learning-based perception models plays a
crucial role in their reliability. Especially in the context of autonomous
driving, downstream tasks like prediction and planning depend on accurate
confidence estimates. In point-wise multiclass classification tasks like
sematic segmentation the model has to deal with heavy class imbalances. Due to
their underrepresentation, the confidence calibration of classes with smaller
instances is challenging but essential, not only for safety reasons. We propose
a metric to measure the confidence calibration quality of a semantic
segmentation model with respect to individual classes. It is calculated by
computing sparsification curves for each class based on the uncertainty
estimates. We use the classification calibration metric to evaluate uncertainty
estimation methods with respect to their confidence calibration of
underrepresented classes. We furthermore suggest a double use for the method to
automatically find label problems to improve the quality of hand- or
auto-annotated datasets.