On the calibration of underrepresented classes in LiDAR-based semantic segmentation

Mariella Dreissig, Florian Piewak, Joschka Boedecker

Keywords: environment perception, semantic segmentation, LiDAR, calibration, uncertainty estimation

TL;DR: Class imbalances have to be adressed when working with models for semantic segmentation, thus, we investigate the influence of class underrepresentation on their calibration.

Abstract: The calibration of deep learning-based perception models plays a crucial role in their reliability. This work focuses on a class-wise evaluation of calibration measures to provide insights one challenge posed onto LiDAR-based semantic segmentation: classes with smaller instances and their subsequent underrepresentation in datasets. Those classes often include VRUs and are thus of particular interest for safety reasons.We evaluate the calibration of underrepresented classes in LiDAR-based semantic segmentation using a metric based on sparsification curves. Thereby we compare three semantic segmentation models of which one is specifically designed to better deal with smaller instances and underrepresented classes.