  The ability to deploy robots that can operate safely in diverse environments
is crucial for developing embodied intelligent agents. As a community, we have
made tremendous progress in within-domain LiDAR semantic segmentation. However,
do these methods generalize across domains? To answer this question, we design
the first experimental setup for studying domain generalization (DG) for LiDAR
semantic segmentation (DG-LSS). Our results confirm a significant gap between
methods, evaluated in a cross-domain setting: for example, a model trained on
the source dataset (SemanticKITTI) obtains $26.53$ mIoU on the target data,
compared to $48.49$ mIoU obtained by the model trained on the target domain
(nuScenes). To tackle this gap, we propose the first method specifically
designed for DG-LSS, which obtains $34.88$ mIoU on the target domain,
outperforming all baselines. Our method augments a sparse-convolutional
encoder-decoder 3D segmentation network with an additional, dense 2D
convolutional decoder that learns to classify a birds-eye view of the point
cloud. This simple auxiliary task encourages the 3D network to learn features
that are robust to sensor placement shifts and resolution, and are transferable
across domains. With this work, we aim to inspire the community to develop and
evaluate future models in such cross-domain conditions.
