| Abstract | A high prediction accuracy of optical printer models is a prerequisite for accurately reproducing visual attributes (color, gloss, translucency) in multimaterial 3D printing. Recently, deep-learning-based models have been proposed, requiring only a moderate number of printed and measured training samples to reach a very high prediction accuracy. In this paper, we present a multi-printer deep learning (MPDL) framework that further improves data efficiency utilizing supporting data from other printers. Experiments on eight multi-material 3D printers demonstrate that the proposed framework can significantly reduce the number of training samples thus the overall printing and measurement efforts. This makes it economically feasible to frequently characterize 3D printers to achieve a high optical reproduction accuracy consistent across different printers and over time, which is crucial for color- and translucency-critical applications. |
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