Recent advances in differentiable rendering, which allow calculating the gradients of 2D pixel values with respect to 3D object models, can be applied to estimation of the model parameters by gradient-based optimization with only 2D supervision. It is easy to incorporate deep neural networks into such an optimization pipeline, allowing the leveraging of deep learning techniques. This also largely reduces the requirement for collecting and annotating 3D data, which is very difficult for applications, for example when constructing geometry from 2D sensors. In this work, we propose a differentiable renderer for sidescan sonar imagery. We further demonstrate its ability to solve the inverse problem of directly reconstructing a 3D seafloor mesh from only 2D sidescan sonar data.

翻译：可变成像的最近进展可以计算3D对象模型2D像素值的梯度,这种进展可用于通过梯度优化来估计模型参数,只有2D监督。很容易将深神经网络纳入这种优化管道,从而能够利用深层学习技术。这也大大降低了收集和注注解3D数据的要求,这对于应用来说非常困难,例如在从 2D 传感器构造几何时。在这项工作中,我们为侧扫声纳图像提出了一个可变成像器。我们进一步证明它有能力从仅2D 侧扫声纳数据直接重建3D海底网的反面问题。