  Automotive simulation can potentially compensate for a lack of training data
in computer vision applications. However, there has been little to no image
quality evaluation of automotive simulation and the impact of optical
degradations on simulation is little explored. In this work, we investigate
Virtual KITTI and the impact of applying variations of Gaussian blur on image
sharpness. Furthermore, we consider object detection, a common computer vision
application on three different state-of-the-art models, thus allowing us to
characterize the relationship between object detection and sharpness. It was
found that while image sharpness (MTF50) degrades from an average of 0.245cy/px
to approximately 0.119cy/px; object detection performance stays largely robust
within 0.58\%(Faster RCNN), 1.45\%(YOLOF) and 1.93\%(DETR) across all
respective held-out test sets.
