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Parallel Pipeline Volume Intersection for Real-Time 3D Shape Reconstruction on a PC Cluster

机译：PC集群实时3D形重建的并行管道卷交叉点

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摘要

The human activity monitoring is one of the major tasks in the field of computer vision. Recently, not only the 2D images but also 3D shapes of a moving person are desired in kinds of cases, such as motion analysis, security monitoring, 3D video creation and so on. In this paper, we propose a parallel pipeline system on a PC cluster for reconstructing the 3D shape of a moving person in real-time. For the 3D shape reconstruction, we have extended the volume intersection method to the 3-base-plane volume intersection. By thus extension, the computation is accelerated greatly for arbitrary camera layouts. We also parallelized the 3-base-plane method and implemented it on a PC cluster. On each node, the pipeline processing is adopted to improve the throughput. To decrease the CPU idle time caused by I/O processing, image capturing, communications over nodes and so on, we implement the pipeline using multiple threads. So that, all stages can be executed concurrently. However, there exists resource conflicts between stages in a real system. To avoid the conflicts while keeping high percentage of CPU running time, we propose a tree structured thread control model. As a result, We achieve the performance as obtaining the full 3D volumes of a moving person at about 12 frames per second, where the voxel size is 5×5×5 [mm{sup}3]. The effectiveness of the thread tree model in such real-time computation is also proved by the experimental results.
机译：人类活动监测是计算机愿景领域的主要任务之一。最近，不仅需要2D图像，而且在各种情况下需要运动人的3D形状，例如运动分析，安全监测，3D视频创建等。在本文中，我们在PC集群上提出了一种并行管道系统，用于实时地重建运动人的3D形状。对于3D形状重建，我们已经将卷交叉点扩展到3基面积卷交叉口。通过延伸，计算对于任意相机布局很大程度上加速。我们还将3个基本平面方法并联化并在PC集群上实现。在每个节点上，采用管道处理来提高吞吐量。为了减少由I / O处理，图像捕获，节点通信等引起的CPU空闲时间，我们使用多个线程实现管道。这样，所有阶段都可以同时执行。但是，实际系统中的阶段之间存在资源冲突。为了避免在保持高百分比的CPU运行时间的同时，我们提出了一条树结构螺纹控制模型。结果，我们将性能达到了在每秒大约12帧的大约12帧之前获得的完整3D体积，其中体素大小为5×5×5 [mm {sup} 3]。还通过实验结果证明了螺纹树模型在这种实时计算中的有效性。

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