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

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 propo...

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Bibliographic Details
Main Authors: Lee, Hongjae, Han, Changwoo, Yoo, Jun-Sang, Jung, Seung-Won
Format: Conference Proceeding
Language:English
Subjects:
Annotations
Computational modeling
Computer network reliability
Computer vision
Conferences
Deep learning
Domain adaptation
GPS
Optical flow
Semantic segmentation
Training
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Summary: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.
ISSN:2473-9944
DOI:10.1109/ICCVW60793.2023.00432