  A number of applications, such as mobile robots or automated vehicles, use
LiDAR sensors to obtain detailed information about their three-dimensional
surroundings. Many methods use image-like projections to efficiently process
these LiDAR measurements and use deep convolutional neural networks to predict
semantic classes for each point in the scan. The spatial stationary assumption
enables the usage of convolutions. However, LiDAR scans exhibit large
differences in appearance over the vertical axis. Therefore, we propose semi
local convolution (SLC), a convolution layer with reduced amount of
weight-sharing along the vertical dimension. We are first to investigate the
usage of such a layer independent of any other model changes. Our experiments
did not show any improvement over traditional convolution layers in terms of
segmentation IoU or accuracy.
