  Understanding and interpreting a 3d environment is a key challenge for
autonomous vehicles. Semantic segmentation of 3d point clouds combines 3d
information with semantics and thereby provides a valuable contribution to this
task. In many real-world applications, point clouds are generated by lidar
sensors in a consecutive fashion. Working with a time series instead of single
and independent frames enables the exploitation of temporal information. We
therefore propose a recurrent segmentation architecture (RNN), which takes a
single range image frame as input and exploits recursively aggregated temporal
information. An alignment strategy, which we call Temporal Memory Alignment,
uses ego motion to temporally align the memory between consecutive frames in
feature space. A Residual Network and ConvGRU are investigated for the memory
update. We demonstrate the benefits of the presented approach on two
large-scale datasets and compare it to several stateof-the-art methods. Our
approach ranks first on the SemanticKITTI multiple scan benchmark and achieves
state-of-the-art performance on the single scan benchmark. In addition, the
evaluation shows that the exploitation of temporal information significantly
improves segmentation results compared to a single frame approach.
