{"title":"缓存的k-d树搜索ICP算法","authors":"A. Nüchter, K. Lingemann, J. Hertzberg","doi":"10.1109/3DIM.2007.15","DOIUrl":null,"url":null,"abstract":"The ICP (iterative closest point) algorithm is the de facto standard for geometric alignment of three-dimensional models when an initial relative pose estimate is available. The basis of ICP is the search for closest points. Since the development of ICP, k-d trees have been used to accelerate the search. This paper presents a novel search procedure, namely cached k-d trees, exploiting iterative behavior of the ICP algorithm. It results in a significant speedup of about 50% as we show in an evaluation using different data sets.","PeriodicalId":442311,"journal":{"name":"Sixth International Conference on 3-D Digital Imaging and Modeling (3DIM 2007)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"180","resultStr":"{\"title\":\"Cached k-d tree search for ICP algorithms\",\"authors\":\"A. Nüchter, K. Lingemann, J. Hertzberg\",\"doi\":\"10.1109/3DIM.2007.15\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The ICP (iterative closest point) algorithm is the de facto standard for geometric alignment of three-dimensional models when an initial relative pose estimate is available. The basis of ICP is the search for closest points. Since the development of ICP, k-d trees have been used to accelerate the search. This paper presents a novel search procedure, namely cached k-d trees, exploiting iterative behavior of the ICP algorithm. It results in a significant speedup of about 50% as we show in an evaluation using different data sets.\",\"PeriodicalId\":442311,\"journal\":{\"name\":\"Sixth International Conference on 3-D Digital Imaging and Modeling (3DIM 2007)\",\"volume\":\"36 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-08-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"180\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Sixth International Conference on 3-D Digital Imaging and Modeling (3DIM 2007)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/3DIM.2007.15\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sixth International Conference on 3-D Digital Imaging and Modeling (3DIM 2007)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/3DIM.2007.15","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The ICP (iterative closest point) algorithm is the de facto standard for geometric alignment of three-dimensional models when an initial relative pose estimate is available. The basis of ICP is the search for closest points. Since the development of ICP, k-d trees have been used to accelerate the search. This paper presents a novel search procedure, namely cached k-d trees, exploiting iterative behavior of the ICP algorithm. It results in a significant speedup of about 50% as we show in an evaluation using different data sets.
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