  Lidar sensors are widely used in various applications, ranging from
scientific fields over industrial use to integration in consumer products. With
an ever growing number of different driver assistance systems, they have been
introduced to automotive series production in recent years and are considered
an important building block for the practical realisation of autonomous
driving. However, due to the potentially large amount of Lidar points per scan,
tailored algorithms are required to identify objects (e.g. pedestrians or
vehicles) with high precision in a very short time. In this work, we propose an
algorithmic approach for real-time instance segmentation of Lidar sensor data.
We show how our method leverages the properties of the Euclidean distance to
retain three-dimensional measurement information, while being narrowed down to
a two-dimensional representation for fast computation. We further introduce
what we call "skip connections", to make our approach robust against
over-segmentation and improve assignment in cases of partial occlusion. Through
detailed evaluation on public data and comparison with established methods, we
show how these aspects enable state-of-the-art performance and runtime on a
single CPU core.
