  LiDAR semantic segmentation for autonomous driving has been a growing field
of interest in the past few years. Datasets and methods have appeared and
expanded very quickly, but methods have not been updated to exploit this new
availability of data and continue to rely on the same classical datasets.
  Different ways of performing LIDAR semantic segmentation training and
inference can be divided into several subfields, which include the following:
domain generalization, the ability to segment data coming from unseen domains ;
source-to-source segmentation, the ability to segment data coming from the
training domain; and pre-training, the ability to create re-usable geometric
primitives.
  In this work, we aim to improve results in all of these subfields with the
novel approach of multi-source training. Multi-source training relies on the
availability of various datasets at training time and uses them together rather
than relying on only one dataset.
  To overcome the common obstacles found for multi-source training, we
introduce the coarse labels and call the newly created multi-source dataset
COLA. We propose three applications of this new dataset that display systematic
improvement over single-source strategies: COLA-DG for domain generalization
(up to +10%), COLA-S2S for source-to-source segmentation (up to +5.3%), and
COLA-PT for pre-training (up to +12%).
