{"title":"骨架生长:一种基于矢量场的三维曲线骨架提取算法","authors":"N. Pantuwong, Masanori Sugimoto","doi":"10.1145/1899950.1899956","DOIUrl":null,"url":null,"abstract":"The vector-field-based method is one of the 3D curve-skeleton extraction algorithms. Typically, critical points in the vector field inside 3D objects are connected to form the curve-skeleton. However, critical points usually do not distribute to all important parts of the 3D object. Therefore, other features are used to produce a reliable result. Although this strategy can deliver a curve-skeleton that captures all of the important parts, the curve-skeleton usually comes with unnecessary segments. This paper proposes the skeleton-growing algorithm that automatically produces the curve-skeleton with small amounts of such segments. It searches for a set of high-curvature boundary voxels as starting points to find a set of suitable seed points that will be used to grow the curve-skeleton. We propose an unnecessary segment removal algorithm that can reduce the skeleton-noise density. A direction-selection algorithm is developed to avoid searching in irrelevant directions. The proposed method can produce a single reliable result curve-skeleton that could be applied in many different applications, including matching, animation, and visualization.","PeriodicalId":354911,"journal":{"name":"ACM SIGGRAPH ASIA 2010 Sketches","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":"{\"title\":\"Skeleton-growing: a vector-field-based 3D curve-skeleton extraction algorithm\",\"authors\":\"N. Pantuwong, Masanori Sugimoto\",\"doi\":\"10.1145/1899950.1899956\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The vector-field-based method is one of the 3D curve-skeleton extraction algorithms. Typically, critical points in the vector field inside 3D objects are connected to form the curve-skeleton. However, critical points usually do not distribute to all important parts of the 3D object. Therefore, other features are used to produce a reliable result. Although this strategy can deliver a curve-skeleton that captures all of the important parts, the curve-skeleton usually comes with unnecessary segments. This paper proposes the skeleton-growing algorithm that automatically produces the curve-skeleton with small amounts of such segments. It searches for a set of high-curvature boundary voxels as starting points to find a set of suitable seed points that will be used to grow the curve-skeleton. We propose an unnecessary segment removal algorithm that can reduce the skeleton-noise density. A direction-selection algorithm is developed to avoid searching in irrelevant directions. The proposed method can produce a single reliable result curve-skeleton that could be applied in many different applications, including matching, animation, and visualization.\",\"PeriodicalId\":354911,\"journal\":{\"name\":\"ACM SIGGRAPH ASIA 2010 Sketches\",\"volume\":\"21 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-12-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"11\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACM SIGGRAPH ASIA 2010 Sketches\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/1899950.1899956\",\"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 ASIA 2010 Sketches","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1899950.1899956","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Skeleton-growing: a vector-field-based 3D curve-skeleton extraction algorithm
The vector-field-based method is one of the 3D curve-skeleton extraction algorithms. Typically, critical points in the vector field inside 3D objects are connected to form the curve-skeleton. However, critical points usually do not distribute to all important parts of the 3D object. Therefore, other features are used to produce a reliable result. Although this strategy can deliver a curve-skeleton that captures all of the important parts, the curve-skeleton usually comes with unnecessary segments. This paper proposes the skeleton-growing algorithm that automatically produces the curve-skeleton with small amounts of such segments. It searches for a set of high-curvature boundary voxels as starting points to find a set of suitable seed points that will be used to grow the curve-skeleton. We propose an unnecessary segment removal algorithm that can reduce the skeleton-noise density. A direction-selection algorithm is developed to avoid searching in irrelevant directions. The proposed method can produce a single reliable result curve-skeleton that could be applied in many different applications, including matching, animation, and visualization.