论文标题
朝着侧扫声纳图像呈现可区分的渲染
Towards Differentiable Rendering for Sidescan Sonar Imagery
论文作者
论文摘要
可区分渲染的最新进展，可以通过仅在2D监督下通过基于梯度的优化来计算2D像素值相对于3D对象模型的梯度。很容易将深层神经网络纳入这样的优化管道，从而可以利用深度学习技术。这也大大减少了收集和注释3D数据的要求，例如，在2D传感器构造几何形状时，这对于应用程序非常困难。在这项工作中，我们为侧can声纳图像提出了一个可区分的渲染器。我们进一步证明了它可以解决仅从2D侧扫描数据数据直接重建3D海底网状网格的逆问题的能力。
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.
