Hierarchical Insights: Exploiting Structural Similarities for Reliable
  3D Semantic Segmentation
Mariella Dreissig Florian Piewak Joschka Boedecker
Safety-critical applications like autonomous driving call for robust 3D
environment perception algorithms which can withstand highly diverse and
ambiguous surroundings. The predictive performance of any classification model
strongly depends on the underlying dataset and the prior knowledge conveyed by
the annotated labels. While the labels provide a basis for the learning
process, they usually fail to represent inherent relations between the classes
- representations, which are a natural element of the human perception system.
We propose a training strategy which enables a 3D LiDAR semantic segmentation
model to learn structural relationships between the different classes through
abstraction. We achieve this by implicitly modeling those relationships through
a learning rule for hierarchical multi-label classification (HMC). With a
detailed analysis we show, how this training strategy not only improves the
model's confidence calibration, but also preserves additional information for
downstream tasks like fusion, prediction and planning.