{"title":"对应自由旋转估计的结构光场设计","authors":"Ian Schillebeeckx, Robert Pless","doi":"10.1109/ICCPHOT.2015.7168376","DOIUrl":null,"url":null,"abstract":"Many vision and augmented reality applications require knowing the rotation of the camera relative to an object or scene. In this paper we propose to create a structured light field designed explicitly to simplify the estimation of camera rotation. The light field is created using a lenticular sheet with a color coded backplane pattern, creating a light field where the observed color depends on the direction of the light. We show that a picture taken within such a light field gives linear constraints on the K-1R matrix that defines the camera calibration and rotation. In this work we derive an optimization that uses these constraints to rapidly estimate rotation, demonstrate a physical prototype and characterize its sensitivity to errors in the camera focal length and camera color sensitivity.","PeriodicalId":302766,"journal":{"name":"2015 IEEE International Conference on Computational Photography (ICCP)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"Structured Light Field Design for Correspondence Free Rotation Estimation\",\"authors\":\"Ian Schillebeeckx, Robert Pless\",\"doi\":\"10.1109/ICCPHOT.2015.7168376\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Many vision and augmented reality applications require knowing the rotation of the camera relative to an object or scene. In this paper we propose to create a structured light field designed explicitly to simplify the estimation of camera rotation. The light field is created using a lenticular sheet with a color coded backplane pattern, creating a light field where the observed color depends on the direction of the light. We show that a picture taken within such a light field gives linear constraints on the K-1R matrix that defines the camera calibration and rotation. In this work we derive an optimization that uses these constraints to rapidly estimate rotation, demonstrate a physical prototype and characterize its sensitivity to errors in the camera focal length and camera color sensitivity.\",\"PeriodicalId\":302766,\"journal\":{\"name\":\"2015 IEEE International Conference on Computational Photography (ICCP)\",\"volume\":\"90 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-04-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 IEEE International Conference on Computational Photography (ICCP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCPHOT.2015.7168376\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE International Conference on Computational Photography (ICCP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCPHOT.2015.7168376","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Structured Light Field Design for Correspondence Free Rotation Estimation
Many vision and augmented reality applications require knowing the rotation of the camera relative to an object or scene. In this paper we propose to create a structured light field designed explicitly to simplify the estimation of camera rotation. The light field is created using a lenticular sheet with a color coded backplane pattern, creating a light field where the observed color depends on the direction of the light. We show that a picture taken within such a light field gives linear constraints on the K-1R matrix that defines the camera calibration and rotation. In this work we derive an optimization that uses these constraints to rapidly estimate rotation, demonstrate a physical prototype and characterize its sensitivity to errors in the camera focal length and camera color sensitivity.