{"title":"增强现实的深度学习","authors":"Jean-François Lalonde","doi":"10.1109/WIO.2018.8643463","DOIUrl":null,"url":null,"abstract":"Augmented reality aims to mix real-world visual content with virtual objects. Achieving realistic results involves solving challenging computer vision tasks, such as tracking real 3D objects and estimating the illumination conditions of a scene. In this short paper, we present how these two challenging tasks can be solved robustly and accurately with deep learning. In both cases, deep convolutional neural networks are trained on large amounts of data, and achieve state-of-the-art results.","PeriodicalId":430979,"journal":{"name":"2018 17th Workshop on Information Optics (WIO)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":"{\"title\":\"Deep Learning for Augmented Reality\",\"authors\":\"Jean-François Lalonde\",\"doi\":\"10.1109/WIO.2018.8643463\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Augmented reality aims to mix real-world visual content with virtual objects. Achieving realistic results involves solving challenging computer vision tasks, such as tracking real 3D objects and estimating the illumination conditions of a scene. In this short paper, we present how these two challenging tasks can be solved robustly and accurately with deep learning. In both cases, deep convolutional neural networks are trained on large amounts of data, and achieve state-of-the-art results.\",\"PeriodicalId\":430979,\"journal\":{\"name\":\"2018 17th Workshop on Information Optics (WIO)\",\"volume\":\"5 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"13\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 17th Workshop on Information Optics (WIO)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WIO.2018.8643463\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 17th Workshop on Information Optics (WIO)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WIO.2018.8643463","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Augmented reality aims to mix real-world visual content with virtual objects. Achieving realistic results involves solving challenging computer vision tasks, such as tracking real 3D objects and estimating the illumination conditions of a scene. In this short paper, we present how these two challenging tasks can be solved robustly and accurately with deep learning. In both cases, deep convolutional neural networks are trained on large amounts of data, and achieve state-of-the-art results.