A novel method to estimate the missing dimension in 2D acoustic images for 3D reconstruction is proposed in this paper. Acoustic cameras can acquire high resolution 2D images in underwater environment insusceptible to water turbidity and light condition. However, the formulation of acoustic images leads to the missing dimension problem. Estimating the unknown elevation angle dimension is a difficult task which has recently drawn the attention of researchers. The non-bijective characteristic between 3D points and 2D pixels increases the complexity of the problem. In this paper, a novel elevation angle estimation method is proposed. The method transfers the acoustic view to pseudo front view using a deep neural network. The proposed network can estimate the missing dimension and resolve the non-bijection problem of the 2D-3D correspondence. Because of the difficulty of acquiring depth information in underwater environments, the network is trained using simulated images. To mitigate the sim-real gap, a neural style transfer method is implemented to generate a realistic image dataset for training. Simulation experiments were carried out for evaluation and real data proved the feasibility of the proposed method.

中文翻译：

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使用伪正视图估算2D声学图像中的仰角

本文提出了一种新的估计2D声像中缺失尺寸以进行3D重建的新方法。声学相机可以在水混浊和光照条件不敏感的水下环境中获取高分辨率的2D图像。然而，声学图像的公式化导致尺寸失踪的问题。估计未知的仰角尺寸是一项艰巨的任务，最近引起了研究人员的注意。3D点和2D像素之间的非双射特性增加了问题的复杂性。本文提出了一种新的仰角估计方法。该方法使用深度神经网络将声学视图转换为伪前视图。所提出的网络可以估计丢失的维度并解决2D-3D对应关系的非双射问题。由于难以在水下环境中获取深度信息，因此使用模拟图像来训练网络。为了减轻模拟现实之间的差距，实现了一种神经样式转换方法，以生成用于训练的逼真的图像数据集。仿真实验进行了评估，实际数据证明了该方法的可行性。