M. Burns, Janek Klawe, S. Rusinkiewicz, Adam Finkelstein, D. DeCarlo
{"title":"Line drawings from volume data","authors":"M. Burns, Janek Klawe, S. Rusinkiewicz, Adam Finkelstein, D. DeCarlo","doi":"10.1145/1186822.1073222","DOIUrl":null,"url":null,"abstract":"Renderings of volumetric data have become an important data analysis tool for applications ranging from medicine to scientific simulation. We propose a volumetric drawing system that directly extracts sparse linear features, such as silhouettes and suggestive contours, using a temporally coherent seed-and-traverse framework. In contrast to previous methods based on isosurfaces or nonrefractive transparency, producing these drawings requires examining an asymptotically smaller subset of the data, leading to efficiency on large data sets. In addition, the resulting imagery is often more comprehensible than standard rendering styles, since it focuses attention on important features in the data. We test our algorithms on datasets up to 5123, demonstrating interactive extraction and rendering of line drawings in a variety of drawing styles.","PeriodicalId":211118,"journal":{"name":"ACM SIGGRAPH 2005 Papers","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2005-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"111","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM SIGGRAPH 2005 Papers","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1186822.1073222","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 111
Abstract
Renderings of volumetric data have become an important data analysis tool for applications ranging from medicine to scientific simulation. We propose a volumetric drawing system that directly extracts sparse linear features, such as silhouettes and suggestive contours, using a temporally coherent seed-and-traverse framework. In contrast to previous methods based on isosurfaces or nonrefractive transparency, producing these drawings requires examining an asymptotically smaller subset of the data, leading to efficiency on large data sets. In addition, the resulting imagery is often more comprehensible than standard rendering styles, since it focuses attention on important features in the data. We test our algorithms on datasets up to 5123, demonstrating interactive extraction and rendering of line drawings in a variety of drawing styles.