{"title":"Context-aware Geometric Object Reconstruction for Mobile Education","authors":"Jinxin Zheng, Yongtao Wang, Zhi Tang","doi":"10.1145/2964284.2967244","DOIUrl":null,"url":null,"abstract":"The solid geometric objects in the educational geometric books are usually illustrated as 2D line drawings accompanied with description text. In this paper, we present a method to recover the geometric objects from 2D to 3D. Unlike the previous methods, we not only use the geometric information from the line drawing itself, but also the textual information extracted from its context. The essential of our method is a cost function to mix the two types of information, and we optimize the cost function to identify the geometric object and recover its 3D information. Our method can recover various types of solid geometric objects including straight-edge manifolds and curved objects such as cone, cylinder and sphere. We show that our method performs significantly better compared to the previous ones.","PeriodicalId":140670,"journal":{"name":"Proceedings of the 24th ACM international conference on Multimedia","volume":"25 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 24th ACM international conference on Multimedia","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2964284.2967244","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
The solid geometric objects in the educational geometric books are usually illustrated as 2D line drawings accompanied with description text. In this paper, we present a method to recover the geometric objects from 2D to 3D. Unlike the previous methods, we not only use the geometric information from the line drawing itself, but also the textual information extracted from its context. The essential of our method is a cost function to mix the two types of information, and we optimize the cost function to identify the geometric object and recover its 3D information. Our method can recover various types of solid geometric objects including straight-edge manifolds and curved objects such as cone, cylinder and sphere. We show that our method performs significantly better compared to the previous ones.