Thingi10K: A Dataset of 10,000 3D-Printing Models

TL;DR

This paper introduces Thingi10K, a comprehensive dataset of 10,000 3D printing models from an online database, capturing real-world geometric and contextual features to aid validation of 3D printing algorithms.

Contribution

The paper presents a new large-scale dataset of 3D printing models with detailed analysis and an online interface, addressing the gap in representative benchmarking data for 3D printing research.

Findings

Dataset captures diverse real-world 3D printing models

Analysis shows dataset's models differ from traditional computer graphics models

Online query interface facilitates targeted subset selection

Abstract

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.