  LiDARs and cameras are the two main sensors that are planned to be included
in many announced autonomous vehicles prototypes. Each of the two provides a
unique form of data from a different perspective to the surrounding
environment. In this paper, we explore and attempt to answer the question: is
there an added benefit by fusing those two forms of data for the purpose of
semantic segmentation within the context of autonomous driving? We also attempt
to show at which level does said fusion prove to be the most useful. We
evaluated our algorithms on the publicly available SemanticKITTI dataset. All
fusion models show improvements over the base model, with the mid-level fusion
showing the highest improvement of 2.7% in terms of mean Intersection over
Union (mIoU) metric.
