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구재원(Jaywon Koo),민동보(Dongbo Min) 대한전자공학회 2021 대한전자공학회 학술대회 Vol.2021 No.6
While stereo matching based on deep networks has shown impressive results in daytime images, the performance is significantly degraded in nighttime images due to the lack of training data with ground truth and poor illumination condition. To overcome these issues, numerous methods have been proposed based on image-to-image translation. These approaches, however, often fail to predict accurate depth maps, when a domain gap between source (daytime) and target (nighttime) domains becomes large. In this paper, we propose a novel method for nighttime stereo matching to resolve such a performance degradation of the existing methods by a large domain gap. The large domain gap that often occurs between the day and night images is addressed using a two-step approach that consists of the image-to-image translation and domain adaptation. By utilizing additional pair of nighttime and daytime datasets which have smaller domain gap, our proposed model learns better image-to-image translation networks while jointly trained two domain adaptation networks explore to adapt to domains that have large domain gap. Extensive experiments on various datasets demonstrate that the proposed method outperforms state-of-the-arts approaches for nighttime stereo matching with a meaningful margin.