M. El-Shehaly, D. Gračanin, M. Gad, Junpeng Wang, Hicham G. Elmongui
{"title":"GPU上的实时交互时间校正","authors":"M. El-Shehaly, D. Gračanin, M. Gad, Junpeng Wang, Hicham G. Elmongui","doi":"10.1109/SciVis.2015.7429505","DOIUrl":null,"url":null,"abstract":"The study of physical phenomena and their dynamic evolution is supported by the analysis and visualization of time-enabled data. In many applications, available data are sparsely distributed in the space-time domain, which leads to incomprehensible visualizations. We present an interactive approach for the dynamic tracking and visualization of measured data particles through advection in a simulated flow. We introduce a fully GPU-based technique for efficient spatio-temporal interpolation, using a kd-tree forest for acceleration. As the user interacts with the system using a time slider, particle positions are reconstructed for the time selected by the user. Our results show that the proposed technique achieves highly accurate parallel tracking for thousands of particles. The rendering performance is mainly affected by the size of the query set.","PeriodicalId":123718,"journal":{"name":"2015 IEEE Scientific Visualization Conference (SciVis)","volume":"815 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Real-time interactive time correction on the GPU\",\"authors\":\"M. El-Shehaly, D. Gračanin, M. Gad, Junpeng Wang, Hicham G. Elmongui\",\"doi\":\"10.1109/SciVis.2015.7429505\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The study of physical phenomena and their dynamic evolution is supported by the analysis and visualization of time-enabled data. In many applications, available data are sparsely distributed in the space-time domain, which leads to incomprehensible visualizations. We present an interactive approach for the dynamic tracking and visualization of measured data particles through advection in a simulated flow. We introduce a fully GPU-based technique for efficient spatio-temporal interpolation, using a kd-tree forest for acceleration. As the user interacts with the system using a time slider, particle positions are reconstructed for the time selected by the user. Our results show that the proposed technique achieves highly accurate parallel tracking for thousands of particles. The rendering performance is mainly affected by the size of the query set.\",\"PeriodicalId\":123718,\"journal\":{\"name\":\"2015 IEEE Scientific Visualization Conference (SciVis)\",\"volume\":\"815 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-10-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 IEEE Scientific Visualization Conference (SciVis)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SciVis.2015.7429505\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE Scientific Visualization Conference (SciVis)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SciVis.2015.7429505","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The study of physical phenomena and their dynamic evolution is supported by the analysis and visualization of time-enabled data. In many applications, available data are sparsely distributed in the space-time domain, which leads to incomprehensible visualizations. We present an interactive approach for the dynamic tracking and visualization of measured data particles through advection in a simulated flow. We introduce a fully GPU-based technique for efficient spatio-temporal interpolation, using a kd-tree forest for acceleration. As the user interacts with the system using a time slider, particle positions are reconstructed for the time selected by the user. Our results show that the proposed technique achieves highly accurate parallel tracking for thousands of particles. The rendering performance is mainly affected by the size of the query set.