Raehyuk Jung, Aiden Seung Joon Lee, Amirsaman Ashtari, J. Bazin
{"title":"Deep360Up: A Deep Learning-Based Approach for Automatic VR Image Upright Adjustment","authors":"Raehyuk Jung, Aiden Seung Joon Lee, Amirsaman Ashtari, J. Bazin","doi":"10.1109/VR.2019.8798326","DOIUrl":null,"url":null,"abstract":"Spherical VR cameras can capture high-quality immersive VR images with a 360° field of view. However, in practice, when the camera orientation is not straight, the acquired VR image appears tilted when displayed on a VR headset, which diminishes the quality of the VR experience. To overcome this problem, we present a deep learning-based approach that can automatically estimate the orientation of a VR image and return its upright version. In contrast to existing methods, our approach does not require the presence of lines or horizon in the image, and thus can be applied on a wide range of scenes. Extensive experiments and comparisons with state-of-the-art methods have successfully confirmed the validity of our approach.","PeriodicalId":315935,"journal":{"name":"2019 IEEE Conference on Virtual Reality and 3D User Interfaces (VR)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"20","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE Conference on Virtual Reality and 3D User Interfaces (VR)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/VR.2019.8798326","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 20
Abstract
Spherical VR cameras can capture high-quality immersive VR images with a 360° field of view. However, in practice, when the camera orientation is not straight, the acquired VR image appears tilted when displayed on a VR headset, which diminishes the quality of the VR experience. To overcome this problem, we present a deep learning-based approach that can automatically estimate the orientation of a VR image and return its upright version. In contrast to existing methods, our approach does not require the presence of lines or horizon in the image, and thus can be applied on a wide range of scenes. Extensive experiments and comparisons with state-of-the-art methods have successfully confirmed the validity of our approach.