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

Source: [On the calibration of underrepresented classes in LiDAR-based semantic segmentation](<https://www.sota2.com/research/paper/68fa6da01e209131cea3af83>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T00:39:21.820Z

Authors: Mariella Dreissig, Florian Piewak, Joschka Boedecker

Published: 2022-10-13T07:49:24.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2210.06811>)

[PDF](<https://arxiv.org/pdf/2210.06811.pdf>)