  In this work, we propose an extension of conventional image data by an
additional channel in which the associated projection properties are encoded.
This addresses the issue of sensor-dependent object representation in
projection-based sensors, such as LiDAR, which can lead to distorted physical
and geometric properties due to variations in sensor resolution and field of
view. To that end, we propose an architecture for processing this data in an
instance segmentation framework. We focus specifically on LiDAR as a key sensor
modality for machine vision tasks and highly automated driving (HAD). Through
an experimental setup in a controlled synthetic environment, we identify a bias
on sensor resolution and field of view and demonstrate that our proposed method
can reduce said bias for the task of LiDAR instance segmentation. Furthermore,
we define our method such that it can be applied to other projection-based
sensors, such as cameras. To promote transparency, we make our code and dataset
publicly available. This method shows the potential to improve performance and
robustness in various machine vision tasks that utilize projection-based
sensors.
