  Safety-critical applications such as autonomous driving require robust 3D
environment perception algorithms capable of handling diverse and ambiguous
surroundings. The predictive performance of classification models is heavily
influenced by the dataset and the prior knowledge provided by the annotated
labels. While labels guide the learning process, they often fail to capture the
inherent relationships between classes that are naturally understood by humans.
We propose a training strategy for a 3D LiDAR semantic segmentation model that
learns structural relationships between classes through abstraction. This is
achieved by implicitly modeling these relationships using a learning rule for
hierarchical multi-label classification (HMC). Our detailed analysis
demonstrates that this training strategy not only improves the model's
confidence calibration but also retains additional information useful for
downstream tasks such as fusion, prediction, and planning.
