  Autonomous driving is becoming a reality, yet vehicles still need to rely on
complex sensor fusion to understand the scene they act in. The ability to
discern static environment and dynamic entities provides a comprehension of the
road layout that poses constraints to the reasoning process about moving
objects. We pursue this through a GAN-based semantic segmentation inpainting
model to remove all dynamic objects from the scene and focus on understanding
its static components such as streets, sidewalks and buildings. We evaluate
this task on the Cityscapes dataset and on a novel synthetically generated
dataset obtained with the CARLA simulator and specifically designed to
quantitatively evaluate semantic segmentation inpaintings. We compare our
methods with a variety of baselines working both in the RGB and segmentation
domains.
