  State-of-the-art multimodal semantic segmentation strategies combining LiDAR
and color data are usually designed on top of asymmetric information-sharing
schemes and assume that both modalities are always available. This strong
assumption may not hold in real-world scenarios, where sensors are prone to
failure or can face adverse conditions that make the acquired information
unreliable. This problem is exacerbated when continual learning scenarios are
considered since they have stringent data reliability constraints. In this
work, we re-frame the task of multimodal semantic segmentation by enforcing a
tightly coupled feature representation and a symmetric information-sharing
scheme, which allows our approach to work even when one of the input modalities
is missing. We also introduce an ad-hoc class-incremental continual learning
scheme, proving our approach's effectiveness and reliability even in
safety-critical settings, such as autonomous driving. We evaluate our approach
on the SemanticKITTI dataset, achieving impressive performances.
