{"type":"ScholarlyArticle","title":"Self-Supervised Monocular Depth Estimation in the Dark: Towards Data Distribution Compensation","authors":[{"name":"Haolin Yang","slug":"haolin-yang","given":"Haolin","family":"Yang","affiliation":"East China University of Science and Technology"},{"name":"Chaoqiang Zhao","slug":"chaoqiang-zhao","given":"Chaoqiang","family":"Zhao","affiliation":"East China University of Science and Technology"},{"name":"Lu Sheng","slug":"lu-sheng","given":"Lu","family":"Sheng","affiliation":"Beihang University"},{"name":"Yang Tang","slug":"yang-tang","given":"Yang","family":"Tang","affiliation":"East China University of Science and Technology"}],"abstract":"Nighttime self-supervised monocular depth estimation has received increasing attention in recent years. However, using night images for self-supervision is unreliable because the photometric consistency assumption is usually violated in the videos taken under complex lighting conditions. Even with domain adaptation or photometric loss repair, performance is still limited by the poor supervision of night images on trainable networks. In this paper, we propose a self-supervised nighttime monocular depth estimation method that does not use any night images during training. Our framework utilizes day images as a stable source for self-supervision and applies physical priors (e.g., wave optics, reflection model and read-shot noise model) to compensate for some key day-night differences. With day-to-night data distribution compensation, our framework can be trained in an efficient one-stage self-supervised manner. Though no nighttime images are considered during training, qualitative and quantitative results demonstrate that our method achieves SoTA depth estimating results on the challenging nuScenes-Night and RobotCar-Night compared with existing methods.","doi":"10.24963/ijcai.2024/173","url":"https://ijcai.org/proceedings/2024/173","pdf":"https://ijcai.org/proceedings/2024/0173.pdf","markdown":"https://ijcai.org/proceedings/2024/173.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/173","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Computer Vision","track":"Main Track","number":173,"pages":{"first":1561,"last":1569},"keywords":["Computer Vision: CV: 3D computer vision","Computer Vision: CV: Scene analysis and understanding","Computer Vision: CV: Other","Computer Vision: CV: Transfer, low-shot, semi- and un- supervised learning"],"videos":[],"references":[{"position":1,"raw":"[Azuma et al., 2001] Ronald Azuma, Yohan Baillot, Rein- hold Behringer, Steven Feiner, Simon Julier, and Blair MacIntyre. 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