Balancing performance and effort in deep learning via the fusion of real and synthetic cultural heritage photogrammetry training sets

Cultural heritage presents both challenges and opportunities for the adoption and use of deep learning in 3D digitisation and digitalisation endeavours. While unique features in terms of the identity of artefacts are important factors that can contribute to training performance in deep learning algorithms, challenges remain with regards to the laborious efforts in our ability to obtain adequate datasets that would both provide for the diversity of imageries, and across the range of multi-facet images for each object in use. One solution, and perhaps an important step towards the broader applicability of deep learning in the field of digital heritage is the fusion of both real and virtual datasets via the automated creation of diverse datasets that covers multiple views of individual objects over a range of diversified objects in the training pipeline, all facilitated by closerange photogrammetry generated 3D objects. The question is the ratio of the combination of real and synthetic imageries in which an inflection point occurs whereby performance is reduced. In this research, we attempt to reduce the need for manual labour by leveraging the flexibility provided for in automated data generation via close-range photogrammetry models with a view for future deep learning facilitated cultural heritage activities, such as digital identification, sorting, asset management and categorisation.