  The calibration of deep learning-based perception models plays a crucial role
in their reliability. Our work focuses on a class-wise evaluation of several
model's confidence performance for LiDAR-based semantic segmentation with the
aim of providing insights into the calibration of underrepresented classes.
Those classes often include VRUs and are thus of particular interest for safety
reasons. With the help of a metric based on sparsification curves we compare
the calibration abilities of three semantic segmentation models with different
architectural concepts, each in a in deterministic and a probabilistic version.
By identifying and describing the dependency between the predictive performance
of a class and the respective calibration quality we aim to facilitate the
model selection and refinement for safety-critical applications.
