Discretized techniques for vector tomographic reconstructions are prone to producing artifacts in the reconstructions. The quality of these reconstructions may further deteriorate as the amount of noise increases. In this work, we instead model the underlying vector fields using smooth neural fields. Owing to the fact that the activation functions in the neural network may be chosen to be smooth and the domain is no longer pixelated, the model results in high-quality reconstructions, even under presence of noise. In the case where we have underlying global continuous symmetry, we find that the neural network substantially improves the accuracy of the reconstruction over the existing techniques.

翻译：离散化的矢量层析重建技术容易在重建结果中产生伪影。随着噪声水平的增加，这些重建的质量可能进一步恶化。在本工作中，我们转而使用平滑的神经场对底层矢量场进行建模。由于神经网络中的激活函数可选择为平滑函数，且定义域不再被像素化，该模型即使在噪声存在的情况下也能产生高质量的重建结果。在底层存在全局连续对称性的情况下，我们发现神经网络相较于现有技术能显著提升重建的精度。