Fast 3D reconstruction of human shape and motion tracking by parallel fast level set method
Fast 3D reconstruction of human shape and motion tracking by parallel fast level set method
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DOI：
10.1109/robot.2008.4543332
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发表时间：
2008-05
期刊：
影响因子：
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通讯作者：
Y. Iwashita;R. Kurazume;K. Hara;S. Uchida;K. Morooka;T. Hasegawa
中科院分区：
文献类型：
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作者：
Y. Iwashita;R. Kurazume;K. Hara;S. Uchida;K. Morooka;T. Hasegawa
本文提出了一种水平集方法的并行算法——并行快速水平集方法，并将其应用于人体形状和运动的实时三维重建。快速水平集方法是水平集方法的一种有效实现算法，已应用于视频图像中的目标跟踪和多台立体摄像机的三维形状重建等应用。在本文中，我们在PC集群上实现了快速水平集方法，并开发了一个用于任意视点图像合成的实时运动捕捉系统。为了在PC集群上获得高性能，高效的负载均衡和资源分配算法是关键问题。提出了一种基于物体边界运动方向估计的载荷分布优化技术。该技术在快速水平集方法框架下估计边界运动，并根据估计的边界运动和当前负载平衡预测和执行最优负载分配。利用该系统成功地进行了人体形状重建和任意视点图像合成实验。
This paper presents a parallel algorithm of the Level Set Method named the Parallel Fast Level Set Method, and its application for real-time 3D reconstruction of human shape and motion. The Fast Level Set Method is an efficient implementation algorithm of the Level Set Method and has been applied to several applications such as object tracking in video images and 3D shape reconstruction using multiple stereo cameras. In this paper, we implement the Fast Level Set Method on a PC cluster and develop a real-time motion capture system for arbitrary viewpoint image synthesis. To obtain high performance on a PC cluster, efficient load-balancing and resource allocation algorithms are crucial problems. We develop a novel optimization technique of load distribution based on the estimation of moving direction of object boundaries. In this technique, the boundary motion is estimated in the framework of the Fast Level Set Method, and the optimum load distribution is predicted and performed according to the estimated boundary motion and the current load balance. Experiments of human shape reconstruction and arbitrary viewpoint image synthesis using the proposed system are successfully carried out.