{"title":"动态场景中多相机设置的鲁棒帧配准","authors":"Xu Zhao, Zhong Zhou, Y. Duan, Wei Wu","doi":"10.1109/ICTAI.2012.58","DOIUrl":null,"url":null,"abstract":"In this paper, we propose a novel method to register frames from multiple cameras into a consistent global scale. Assuming a moving object is observed in multiple camera setups, we use initial frames to create a global reference structure where the pose variation of each new frame is estimated using a RANSAC-based registration algorithm. We further combine the registration method with other state-of the-art techniques to build a high quality 3D reconstruction system with a smaller number of cameras than used by more traditional methods. Experimental results show that our method performs better and is more economical than the registration of separate monocular structures from motion methods. 3D reconstruction results on various challenging real world multi-camera video datasets also illustrate the feasibility and robustness of our method.","PeriodicalId":155588,"journal":{"name":"2012 IEEE 24th International Conference on Tools with Artificial Intelligence","volume":"53 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Robust Frame Registration for Multiple Camera Setups in Dynamic Scenes\",\"authors\":\"Xu Zhao, Zhong Zhou, Y. Duan, Wei Wu\",\"doi\":\"10.1109/ICTAI.2012.58\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we propose a novel method to register frames from multiple cameras into a consistent global scale. Assuming a moving object is observed in multiple camera setups, we use initial frames to create a global reference structure where the pose variation of each new frame is estimated using a RANSAC-based registration algorithm. We further combine the registration method with other state-of the-art techniques to build a high quality 3D reconstruction system with a smaller number of cameras than used by more traditional methods. Experimental results show that our method performs better and is more economical than the registration of separate monocular structures from motion methods. 3D reconstruction results on various challenging real world multi-camera video datasets also illustrate the feasibility and robustness of our method.\",\"PeriodicalId\":155588,\"journal\":{\"name\":\"2012 IEEE 24th International Conference on Tools with Artificial Intelligence\",\"volume\":\"53 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-11-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 IEEE 24th International Conference on Tools with Artificial Intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICTAI.2012.58\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE 24th International Conference on Tools with Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICTAI.2012.58","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Robust Frame Registration for Multiple Camera Setups in Dynamic Scenes
In this paper, we propose a novel method to register frames from multiple cameras into a consistent global scale. Assuming a moving object is observed in multiple camera setups, we use initial frames to create a global reference structure where the pose variation of each new frame is estimated using a RANSAC-based registration algorithm. We further combine the registration method with other state-of the-art techniques to build a high quality 3D reconstruction system with a smaller number of cameras than used by more traditional methods. Experimental results show that our method performs better and is more economical than the registration of separate monocular structures from motion methods. 3D reconstruction results on various challenging real world multi-camera video datasets also illustrate the feasibility and robustness of our method.