  Using deep learning, 3D autonomous driving semantic segmentation has become a
well-studied subject, with methods that can reach very high performance.
Nonetheless, because of the limited size of the training datasets, these models
cannot see every type of object and scene found in real-world applications. The
ability to be reliable in these various unknown environments is called
\textup{domain generalization}.
  Despite its importance, domain generalization is relatively unexplored in the
case of 3D autonomous driving semantic segmentation. To fill this gap, this
paper presents the first benchmark for this application by testing
state-of-the-art methods and discussing the difficulty of tackling Laser
Imaging Detection and Ranging (LiDAR) domain shifts.
  We also propose the first method designed to address this domain
generalization, which we call 3DLabelProp. This method relies on leveraging the
geometry and sequentiality of the LiDAR data to enhance its generalization
performances by working on partially accumulated point clouds. It reaches a
mean Intersection over Union (mIoU) of 50.4% on SemanticPOSS and of 55.2% on
PandaSet solid-state LiDAR while being trained only on SemanticKITTI, making it
the state-of-the-art method for generalization (+5% and +33% better,
respectively, than the second best method).
  The code for this method is available on GitHub:
https://github.com/JulesSanchez/3DLabelProp.
