Kwamina Edum-Fotwe, P. Shepherd, Matthew Brown, Dan Harper, Richard Dinnis
{"title":"快速，无约束，近似l形方法","authors":"Kwamina Edum-Fotwe, P. Shepherd, Matthew Brown, Dan Harper, Richard Dinnis","doi":"10.1145/2945078.2945163","DOIUrl":null,"url":null,"abstract":"This simple paper describes an intuitive data-driven approach to reconstructing architectural building-footprints from structured or unstructured 2D pointsets. The function is fast, accurate and unconstrained. Further unlike the prevalent L-Shape detectors predicated on a shape's skeletal descriptor [Szeliski 2010], the method is robust to sensing noise at the boundary of a 2D pointset.","PeriodicalId":417667,"journal":{"name":"ACM SIGGRAPH 2016 Posters","volume":"36 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Quick, unconstrained, approximate l-shape method\",\"authors\":\"Kwamina Edum-Fotwe, P. Shepherd, Matthew Brown, Dan Harper, Richard Dinnis\",\"doi\":\"10.1145/2945078.2945163\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This simple paper describes an intuitive data-driven approach to reconstructing architectural building-footprints from structured or unstructured 2D pointsets. The function is fast, accurate and unconstrained. Further unlike the prevalent L-Shape detectors predicated on a shape's skeletal descriptor [Szeliski 2010], the method is robust to sensing noise at the boundary of a 2D pointset.\",\"PeriodicalId\":417667,\"journal\":{\"name\":\"ACM SIGGRAPH 2016 Posters\",\"volume\":\"36 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-07-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACM SIGGRAPH 2016 Posters\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2945078.2945163\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM SIGGRAPH 2016 Posters","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2945078.2945163","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This simple paper describes an intuitive data-driven approach to reconstructing architectural building-footprints from structured or unstructured 2D pointsets. The function is fast, accurate and unconstrained. Further unlike the prevalent L-Shape detectors predicated on a shape's skeletal descriptor [Szeliski 2010], the method is robust to sensing noise at the boundary of a 2D pointset.
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