Abstract

The emergence of consumer-grade 3D printing has democratized innovation through online design-sharing platforms like Thingiverse. We introduce a novel multimodal dataset called “ThingiPano”, a large-scale collection containing multi-view 2D panoramic representations of over a million 3D files (n=1,816,295) with associated user-uploaded images (n=1,816,295), design metadata (n=1,017,687), and user meta- data (n=283,873) from Thingiverse. In this paper, we ex- hibit how ThingiPano’s metadata can facilitate greater un- derstanding of how 3D printing designs are fabricated, by who, and for what purpose. We demonstrate how this novel multimodal dataset is sufficient for self-supervised machine learning methodologies. Such methodologies have the potential to facilitate broader reuse of 3D printable designs, through improved multimodal classification and retrieval in various applications from online file-sharing platforms to design-tools. Visit https://github.com/Alexander-Berman/ThingiPano for more info and to download