  Etruscan mirrors constitute a significant category within Etruscan art and,
therefore, undergo systematic examinations to obtain insights into ancient
times. A crucial aspect of their analysis involves the labor-intensive task of
manually tracing engravings from the backside. Additionally, this task is
inherently challenging due to the damage these mirrors have sustained,
introducing subjectivity into the process. We address these challenges by
automating the process through photometric-stereo scanning in conjunction with
deep segmentation networks which, however, requires effective usage of the
limited data at hand. We accomplish this by incorporating predictions on a
per-patch level, and various data augmentations, as well as exploring
self-supervised learning. Compared to our baseline, we improve predictive
performance w.r.t. the pseudo-F-Measure by around 16%. When assessing
performance on complete mirrors against a human baseline, our approach yields
quantitative similar performance to a human annotator and significantly
outperforms existing binarization methods. With our proposed methodology, we
streamline the annotation process, enhance its objectivity, and reduce overall
workload, offering a valuable contribution to the examination of these
historical artifacts and other non-traditional documents.
