  Semantic Image Segmentation facilitates a multitude of real-world
applications ranging from autonomous driving over industrial process
supervision to vision aids for human beings. These models are usually trained
in a supervised fashion using example inputs. Distribution Shifts between these
examples and the inputs in operation may cause erroneous segmentations. The
robustness of semantic segmentation models against distribution shifts caused
by differing camera or lighting setups, lens distortions, adversarial inputs
and image corruptions has been topic of recent research. However, robustness
against spatially varying radial distortion effects that can be caused by
uneven glass structures (e.g. windows) or the chaotic refraction in heated air
has not been addressed by the research community yet. We propose a method to
synthetically augment existing datasets with spatially varying distortions. Our
experiments show, that these distortion effects degrade the performance of
state-of-the-art segmentation models. Pretraining and enlarged model capacities
proof to be suitable strategies for mitigating performance degradation to some
degree, while fine-tuning on distorted images only leads to marginal
performance improvements.
