  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.
