  This paper investigates the impact of LiDAR configuration shifts on the
performance of 3D LiDAR point cloud semantic segmentation models, a topic not
extensively studied before. We explore the effect of using different LiDAR
channels when training and testing a 3D LiDAR point cloud semantic segmentation
model, utilizing Cylinder3D for the experiments. A Cylinder3D model is trained
and tested on simulated 3D LiDAR point cloud datasets created using the
Mississippi State University Autonomous Vehicle Simulator (MAVS) and 32, 64
channel 3D LiDAR point clouds of the RELLIS-3D dataset collected in a
real-world off-road environment. Our experimental results demonstrate that
sensor and spatial domain shifts significantly impact the performance of
LiDAR-based semantic segmentation models. In the absence of spatial domain
changes between training and testing, models trained and tested on the same
sensor type generally exhibited better performance. Moreover, higher-resolution
sensors showed improved performance compared to those with lower-resolution
ones. However, results varied when spatial domain changes were present. In some
cases, the advantage of a sensor's higher resolution led to better performance
both with and without sensor domain shifts. In other instances, the higher
resolution resulted in overfitting within a specific domain, causing a lack of
generalization capability and decreased performance when tested on data with
different sensor configurations.
