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Large-Scale Point Cloud Based Volume Analysis Acceleration Based on UAV Images

机译:基于无人机图像的大规模点云体积分析加速

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Small-sized drones, either having classical fixed-wing design or the so rapidly widespread rotorcraft design, are now revolutionizing the complete work process of aerial surveys. In the paper, we will discuss the possibilities of the quality characteristics of large-scale point cloud processing, already introduced in our previous work, and primarily the reduction of the execution time of the large-point cloud processing presented in our previous work. Based on the work process developed by us, we survey the work area, typically a quarry, using UAVs, and then we create a high resolution 3D point cloud out of the pictures. Then, the time series photos are fitted to each other using the ICP algorithm and we conduct the volume analysis. In the present case, the ICP algorithm and volume calculation were very time-consuming operations, as every 3D model was made up of ~20 million points. The two leading graphics processing unit producers of the market have made the resources of the hardware available for general-purpose calculations. In this way, we can see good opportunity in the implementation of these time-consuming calculations into the target hardware.
机译:小型无人机,无论是经典的固定翼设计还是迅速普及的旋翼飞机设计,都正在彻底改变航空测量的整个工作过程。在本文中,我们将讨论在先前的工作中已经介绍的大规模点云处理的质量特征的可能性,并且主要是在我们先前的工作中提出的大点云处理的执行时间的减少。根据我们开发的工作流程,我们使用无人机调查工作区域(通常是采石场),然后从图片中创建高分辨率3D点云。然后,使用ICP算法将时间序列照片彼此拟合,然后进行体积分析。在当前情况下,ICP算法和体积计算非常耗时,因为每个3D模型都由约2000万个点组成。市场上两家领先的图形处理单元生产商已将硬件资源用于通用计算。通过这种方式,我们可以看到在目标硬件中实施这些耗时的计算的良好机会。

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