Three dimensional electron back-scattered diffraction (EBSD) microscopy is a
critical tool in many applications in materials science, yet its data quality
can fluctuate greatly during the arduous collection process, particularly via
serial-sectioning. Fortunately, 3D EBSD data is inherently sequential, opening
up the opportunity to use transformers, state-of-the-art deep learning
architectures that have made breakthroughs in a plethora of domains, for data
processing and recovery. To be more robust to errors and accelerate this 3D
EBSD data collection, we introduce a two step method that recovers missing
slices in an 3D EBSD volume, using an efficient transformer model and a
projection algorithm to process the transformer's outputs. Overcoming the
computational and practical hurdles of deep learning with scarce high
dimensional data, we train this model using only synthetic 3D EBSD data with
self-supervision and obtain superior recovery accuracy on real 3D EBSD data,
compared to existing methods.

通过使用高效的变压器模型和投影算法处理变压器的输出，克服了深度学习与稀缺高维数据的计算和实际障碍，并使用自我监督的合成三维 EBSD 数据训练该模型，在真实的三维 EBSD 数据中获得了比现有方法更高的恢复精度。