  Recent advances in autonomous robotic technologies have highlighted the
growing need for precise environmental analysis. LiDAR semantic segmentation
has gained attention to accomplish fine-grained scene understanding by acting
directly on raw content provided by sensors. Recent solutions showed how
different learning techniques can be used to improve the performance of the
model, without any architectural or dataset change. Following this trend, we
present a coarse-to-fine setup that LEArns from classification mistaKes (LEAK)
derived from a standard model. First, classes are clustered into macro groups
according to mutual prediction errors; then, the learning process is
regularized by: (1) aligning class-conditional prototypical feature
representation for both fine and coarse classes, (2) weighting instances with a
per-class fairness index. Our LEAK approach is very general and can be
seamlessly applied on top of any segmentation architecture; indeed,
experimental results showed that it enables state-of-the-art performances on
different architectures, datasets and tasks, while ensuring more balanced
class-wise results and faster convergence.
