  Semantic segmentation for autonomous driving should be robust against various
in-the-wild environments. Nighttime semantic segmentation is especially
challenging due to a lack of annotated nighttime images and a large domain gap
from daytime images with sufficient annotation. In this paper, we propose a
novel GPS-based training framework for nighttime semantic segmentation. Given
GPS-aligned pairs of daytime and nighttime images, we perform cross-domain
correspondence matching to obtain pixel-level pseudo supervision. Moreover, we
conduct flow estimation between daytime video frames and apply GPS-based
scaling to acquire another pixel-level pseudo supervision. Using these pseudo
supervisions with a confidence map, we train a nighttime semantic segmentation
network without any annotation from nighttime images. Experimental results
demonstrate the effectiveness of the proposed method on several nighttime
semantic segmentation datasets. Our source code is available at
https://github.com/jimmy9704/GPS-GLASS.
