Papers › GPS-GLASS: Learning Nighttime Semantic Segmentation Using Daytime Video and GPS data

GPS-GLASS: Learning Nighttime Semantic Segmentation Using Daytime Video and GPS data

27 Jul 2022arXiv:2207.13297archive 2025-07-28

Hongjae Lee, Changwoo Han, Jun-Sang Yoo, Seung-Won Jung

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.

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Tasks

Autonomous Driving Dense Pixel Correspondence Estimation Domain Adaptation Optical Flow Estimation Segmentation Semantic Segmentation

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Semantic Segmentation	Dark Zurich	GPS-GLASS	mIoU	46.4	#8 of 14	Archive leaderboard	report

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