  Point clouds are a key modality used for perception in autonomous vehicles,
providing the means for a robust geometric understanding of the surrounding
environment. However despite the sensor outputs from autonomous vehicles being
naturally temporal in nature, there is still limited exploration of exploiting
point cloud sequences for 3D seman-tic segmentation. In this paper we propose a
novel Sparse Temporal Local Attention (STELA) module which aggregates
intermediate features from a local neighbourhood in previous point cloud frames
to provide a rich temporal context to the decoder. Using the sparse local
neighbourhood enables our approach to gather features more flexibly than those
which directly match point features, and more efficiently than those which
perform expensive global attention over the whole point cloud frame. We achieve
a competitive mIoU of 64.3% on the SemanticKitti dataset, and demonstrate
significant improvement over the single-frame baseline in our ablation studies.
