  Semantic segmentation is a core ability required by autonomous agents, as
being able to distinguish which parts of the scene belong to which object class
is crucial for navigation and interaction with the environment. Approaches
which use only one time-step of data cannot distinguish between moving objects
nor can they benefit from temporal integration. In this work, we extend a
backbone LatticeNet to process temporal point cloud data. Additionally, we take
inspiration from optical flow methods and propose a new module called Abstract
Flow which allows the network to match parts of the scene with similar abstract
features and gather the information temporally. We obtain state-of-the-art
results on the SemanticKITTI dataset that contains LiDAR scans from real urban
environments. We share the PyTorch implementation of TemporalLatticeNet at
https://github.com/AIS-Bonn/temporal_latticenet .
