We present an algorithm to reconstruct the three-dimensional positions of particles in a dense cloud of particles in a dusty plasma using a convolutional neural network. The approach is found to be very fast and yields a relatively high accuracy. In this paper, we describe and examine the approach regarding the particle number and the reconstruction accuracy using synthetic data and experimental data. To show the applicability of the approach the 3D positions of particles in a dense dust cloud in a dusty plasma under weightlessness are reconstructed from stereoscopic camera images using the prescribed neural network.

中文翻译：

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使用卷积神经网络的快速 3D 粒子重建：应用于多尘等离子体

我们提出了一种算法，使用卷积神经网络重建尘埃等离子体中密集粒子云中粒子的三维位置。发现该方法非常快并且产生相对较高的准确度。在本文中，我们使用合成数据和实验数据描述和检验了有关粒子数和重建精度的方法。为了展示该方法的适用性，使用规定的神经网络从立体相机图像重建了失重状态下尘埃等离子体中密集尘埃云中粒子的 3D 位置。