{"title":"基于深度RNN模型的虚拟现实6DoF跟踪","authors":"Yun-Kai Chang, Mai-Keh Chen, Yun-Lun Li, Hao-Ting Li, Chen-Kuo Chiang","doi":"10.1109/IS3C50286.2020.00057","DOIUrl":null,"url":null,"abstract":"In this paper, a novel coordinates tracking method is proposed for Virtual Reality (VR) environment using sensor signals. The purpose is to extend movement tracking in VR from 3 Degrees of Freedom (DOF) of rotation to 6DOF of position plus rotation. As a result, we can track VR coordinates without using controller or handler provided by VR devices. An RNN-based model is proposed to predict displacement of positions in each timestamp given measured acceleration and Euler angles from sensor signals. Experiments demonstrate that it is effective to predict correct position displacement, which not only models the relationship between sensor signals and displacement but also handles the cumulative errors during tracking.","PeriodicalId":143430,"journal":{"name":"2020 International Symposium on Computer, Consumer and Control (IS3C)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"6DoF Tracking in Virtual Reality by Deep RNN Model\",\"authors\":\"Yun-Kai Chang, Mai-Keh Chen, Yun-Lun Li, Hao-Ting Li, Chen-Kuo Chiang\",\"doi\":\"10.1109/IS3C50286.2020.00057\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, a novel coordinates tracking method is proposed for Virtual Reality (VR) environment using sensor signals. The purpose is to extend movement tracking in VR from 3 Degrees of Freedom (DOF) of rotation to 6DOF of position plus rotation. As a result, we can track VR coordinates without using controller or handler provided by VR devices. An RNN-based model is proposed to predict displacement of positions in each timestamp given measured acceleration and Euler angles from sensor signals. Experiments demonstrate that it is effective to predict correct position displacement, which not only models the relationship between sensor signals and displacement but also handles the cumulative errors during tracking.\",\"PeriodicalId\":143430,\"journal\":{\"name\":\"2020 International Symposium on Computer, Consumer and Control (IS3C)\",\"volume\":\"24 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 International Symposium on Computer, Consumer and Control (IS3C)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IS3C50286.2020.00057\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 International Symposium on Computer, Consumer and Control (IS3C)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IS3C50286.2020.00057","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
6DoF Tracking in Virtual Reality by Deep RNN Model
In this paper, a novel coordinates tracking method is proposed for Virtual Reality (VR) environment using sensor signals. The purpose is to extend movement tracking in VR from 3 Degrees of Freedom (DOF) of rotation to 6DOF of position plus rotation. As a result, we can track VR coordinates without using controller or handler provided by VR devices. An RNN-based model is proposed to predict displacement of positions in each timestamp given measured acceleration and Euler angles from sensor signals. Experiments demonstrate that it is effective to predict correct position displacement, which not only models the relationship between sensor signals and displacement but also handles the cumulative errors during tracking.