  Empirically validating new 3D-printing related algorithms and implementations
requires testing data representative of inputs encountered \emph{in the wild}.
An ideal benchmarking dataset should not only draw from the same distribution
of shapes people print in terms of class (e.g., toys, mechanisms, jewelry),
representation type (e.g., triangle soup meshes) and complexity (e.g., number
of facets), but should also capture problems and artifacts endemic to 3D
printing models (e.g., self-intersections, non-manifoldness). We observe that
the contextual and geometric characteristics of 3D printing models differ
significantly from those used for computer graphics applications, not to
mention standard models (e.g., Stanford bunny, Armadillo, Fertility). We
present a new dataset of 10,000 models collected from an online 3D printing
model-sharing database. Via analysis of both geometric (e.g., triangle aspect
ratios, manifoldness) and contextual (e.g., licenses, tags, classes)
characteristics, we demonstrate that this dataset represents a more concise
summary of real-world models used for 3D printing compared to existing
datasets. To facilitate future research endeavors, we also present an online
query interface to select subsets of the dataset according to project-specific
characteristics. The complete dataset and per-model statistical data are freely
available to the public.
