  Self-supervised learning (SSL) has the potential to benefit many
applications, particularly those where manually annotating data is cumbersome.
One such situation is the semantic segmentation of point clouds. In this
context, existing methods employ contrastive learning strategies and define
positive pairs by performing various augmentation of point clusters in a single
frame. As such, these methods do not exploit the temporal nature of LiDAR data.
In this paper, we introduce an SSL strategy that leverages positive pairs in
both the spatial and temporal domain. To this end, we design (i) a
point-to-cluster learning strategy that aggregates spatial information to
distinguish objects; and (ii) a cluster-to-cluster learning strategy based on
unsupervised object tracking that exploits temporal correspondences. We
demonstrate the benefits of our approach via extensive experiments performed by
self-supervised training on two large-scale LiDAR datasets and transferring the
resulting models to other point cloud segmentation benchmarks. Our results
evidence that our method outperforms the state-of-the-art point cloud SSL
methods.
