{"title":"离散数据的k近邻快速搜索算法","authors":"Zhu Ge-jun, M. Changsheng, Xie Feng","doi":"10.1109/FITME.2010.5655716","DOIUrl":null,"url":null,"abstract":"The paper has put forward an improved K-nearest searching algorithm of scattered data, which is significant to the technology of surface recreate of reverse engineering. Firstly, the initial segmentation of point cloud space is made by adopting the traditional block algorithm, and then estimates the average dot pitch of point cloud. Re-divide the point cloud space according to average dot pitch. The block result decreases the searching range of k-nearest neighbor searching algorithm.","PeriodicalId":421597,"journal":{"name":"2010 International Conference on Future Information Technology and Management Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"The K-nearest neighbor fast searching algorithm of scattered data\",\"authors\":\"Zhu Ge-jun, M. Changsheng, Xie Feng\",\"doi\":\"10.1109/FITME.2010.5655716\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The paper has put forward an improved K-nearest searching algorithm of scattered data, which is significant to the technology of surface recreate of reverse engineering. Firstly, the initial segmentation of point cloud space is made by adopting the traditional block algorithm, and then estimates the average dot pitch of point cloud. Re-divide the point cloud space according to average dot pitch. The block result decreases the searching range of k-nearest neighbor searching algorithm.\",\"PeriodicalId\":421597,\"journal\":{\"name\":\"2010 International Conference on Future Information Technology and Management Engineering\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-12-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 International Conference on Future Information Technology and Management Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/FITME.2010.5655716\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 International Conference on Future Information Technology and Management Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FITME.2010.5655716","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The K-nearest neighbor fast searching algorithm of scattered data
The paper has put forward an improved K-nearest searching algorithm of scattered data, which is significant to the technology of surface recreate of reverse engineering. Firstly, the initial segmentation of point cloud space is made by adopting the traditional block algorithm, and then estimates the average dot pitch of point cloud. Re-divide the point cloud space according to average dot pitch. The block result decreases the searching range of k-nearest neighbor searching algorithm.