{"title":"基于CUDA的快速网格相似度测量","authors":"Jie Tang, Gangshan Wu, Boping Xu, Zhongliang Gong","doi":"10.1109/PIC.2010.5687883","DOIUrl":null,"url":null,"abstract":"This paper presented a fast algorithm which could measure similarity between two meshes interactively. The algorithm was based on CUDA (Compute Unified Device Architecture) technology. In order to fully utilize the computing power of GPU, we developed parallel method to construct uniform grid for fast space indexing of triangles. Special data structure was designed on device end to overcome the disadvantage of CUDA that it does not support dynamic allocation of memory. Lots of experiments were carried out and the results verified the effectiveness and efficiency of our algorithm.","PeriodicalId":142910,"journal":{"name":"2010 IEEE International Conference on Progress in Informatics and Computing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Fast mesh similarity measuring based on CUDA\",\"authors\":\"Jie Tang, Gangshan Wu, Boping Xu, Zhongliang Gong\",\"doi\":\"10.1109/PIC.2010.5687883\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presented a fast algorithm which could measure similarity between two meshes interactively. The algorithm was based on CUDA (Compute Unified Device Architecture) technology. In order to fully utilize the computing power of GPU, we developed parallel method to construct uniform grid for fast space indexing of triangles. Special data structure was designed on device end to overcome the disadvantage of CUDA that it does not support dynamic allocation of memory. Lots of experiments were carried out and the results verified the effectiveness and efficiency of our algorithm.\",\"PeriodicalId\":142910,\"journal\":{\"name\":\"2010 IEEE International Conference on Progress in Informatics and Computing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 IEEE International Conference on Progress in Informatics and Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/PIC.2010.5687883\",\"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 IEEE International Conference on Progress in Informatics and Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PIC.2010.5687883","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This paper presented a fast algorithm which could measure similarity between two meshes interactively. The algorithm was based on CUDA (Compute Unified Device Architecture) technology. In order to fully utilize the computing power of GPU, we developed parallel method to construct uniform grid for fast space indexing of triangles. Special data structure was designed on device end to overcome the disadvantage of CUDA that it does not support dynamic allocation of memory. Lots of experiments were carried out and the results verified the effectiveness and efficiency of our algorithm.
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