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.

翻译：三维电子背散射衍射（EBSD）显微镜是材料科学众多应用中的关键工具，然而在其繁琐的采集过程中（尤其是通过连续切片法），数据质量可能大幅波动。幸运的是，三维EBSD数据具有天然的序列特性，这为利用Transformer——一种在多个领域取得突破性进展的先进深度学习架构——进行数据处理与恢复提供了契机。为提升对误差的鲁棒性并加速三维EBSD数据采集，我们提出了一种两步法：首先使用高效的Transformer模型恢复三维EBSD体数据中的缺失切片，随后通过投影算法对Transformer的输出进行处理。为克服在稀缺高维数据场景下应用深度学习的计算与实际障碍，我们仅采用合成三维EBSD数据并通过自监督方式训练该模型，实验表明，与现有方法相比，本方法在真实三维EBSD数据上取得了更优的恢复精度。