ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2014, Vol. 51 ›› Issue (11): 2559-2572.doi: 10.7544/issn1000-1239.2014.20130242

• 图形图像 • 上一篇    下一篇

基于图像空间的三维动态场景远程可视化方法

马志强1,王莉莉1,张鑫维1,柯韦2,赵沁平1   

  1. 1(虚拟现实技术与系统国家重点实验室(北京航空航天大学) 北京 100191);2(澳门理工学院 澳门) (mazhiqiang@vrlab.buaa.edu.cn)
  • 出版日期: 2014-11-01
  • 基金资助: 
    基金项目:国家“八六三”高技术研究发展计划基金项目(2013AA01A604);国家自然科学基金项目(61272349,61190121);澳门科学技术发展基金项目(043/2009/A2)

Remote Visualization for 3D Dynamic Scene Based on Image Space

Ma Zhiqiang1, Wang Lili1, Zhang Xinwei1, Ke Wei2, Zhao Qinping1   

  1. 1(State Key Laboratory of Virtual Reality Technology and Systems (Beihang University), Beijing 100191); 2(Macao Polytechnic Institute, Macao China)
  • Online: 2014-11-01

摘要: 对复杂三维动态场景进行远程可视化,基于顶点运动轨迹的压缩、传输方法需要计算的顶点数量巨大,速度慢.针对这些问题,提出一种基于图像空间的三维动态场景远程可视化方法:首先在服务器端进行先空间后时间的自适应采样,获得多幅动画深度图像.通过使用动画深度图像的采样点代替原始动态场景中的顶点,极大减少需要参与计算的三维点数量;然后在每幅动画深度图像中并行压缩采样点的运动轨迹,有效减少压缩时间;最后将压缩后的动态数据传输到客户端并重构一定时间内的三维动态场景.实验结果表明,算法可以极大提高服务器端数据压缩速度,减少需要传输的数据量,有效降低网络带宽对数据传输的限制.

关键词: 图像空间, 时变数据集, 自适应采样, 动态数据压缩, 远程可视化

Abstract: In remote visualization for complex 3D dynamic scene, compression and transmission for vertex trajectory needs computing huge vertex and has very slow computational speed. In order to solve these problems, we present a remote visualization method for 3D dynamic scene based on image space, which replaces vertex trajectory with sample trajectory to reduce the vertex number in computation and presents a parallel compression method to realize rapid and effect compression for dynamic datasets. First, adaptive sampling algorithm in space and time (time after space) using graphics pipeline and the construction of samples connective information is presented to obtain many animated depth images. Then, the trajectory of samples is compressed in parallel in each animated depth image, which reduces compression time effectively. Finally, compressed dynamic datasets are transferred to the client and 3D dynamic scene over some time is reconstructed. Reconstructed 3D dynamic scene during some time supplies the client with observation in arbitrary angles and has little decline in rendering quality. Exprement results also show that the algorithms realize rapid compression and reduce the number of dynamic datasets greatly, which reduce network latency limitation effectively.

Key words: image space, time-varying datasets, adaptive sampling, dynamic data compression, remote visualization

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