{"title":"从大规模Kinect融合生成拓扑一致的三角形网格","authors":"Tristan Igelbrink, T. Wiemann, J. Hertzberg","doi":"10.1109/ECMR.2015.7324205","DOIUrl":null,"url":null,"abstract":"Generating polygonal maps from RGB-D data is an active field of research in robotic mapping. Kinect Fusion and related algorithms provide means to generate reconstructions of large environments. However, most available implementations generate topological artifacts like redundant vertices and triangles. In this paper we present a novel data structure that allows to generate topologically consistent triangle meshes from RGB-D data without additional filtering.","PeriodicalId":142754,"journal":{"name":"2015 European Conference on Mobile Robots (ECMR)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Generating topologically consistent triangle meshes from large scale Kinect Fusion\",\"authors\":\"Tristan Igelbrink, T. Wiemann, J. Hertzberg\",\"doi\":\"10.1109/ECMR.2015.7324205\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Generating polygonal maps from RGB-D data is an active field of research in robotic mapping. Kinect Fusion and related algorithms provide means to generate reconstructions of large environments. However, most available implementations generate topological artifacts like redundant vertices and triangles. In this paper we present a novel data structure that allows to generate topologically consistent triangle meshes from RGB-D data without additional filtering.\",\"PeriodicalId\":142754,\"journal\":{\"name\":\"2015 European Conference on Mobile Robots (ECMR)\",\"volume\":\"7 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-11-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 European Conference on Mobile Robots (ECMR)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ECMR.2015.7324205\",\"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 European Conference on Mobile Robots (ECMR)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ECMR.2015.7324205","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Generating topologically consistent triangle meshes from large scale Kinect Fusion
Generating polygonal maps from RGB-D data is an active field of research in robotic mapping. Kinect Fusion and related algorithms provide means to generate reconstructions of large environments. However, most available implementations generate topological artifacts like redundant vertices and triangles. In this paper we present a novel data structure that allows to generate topologically consistent triangle meshes from RGB-D data without additional filtering.
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