  Automated shape repair approaches currently lack access to datasets that
describe real-world damaged geometry. We present Fantastic Breaks (and Where to
Find Them:
https://terascale-all-sensing-research-studio.github.io/FantasticBreaks), a
dataset containing scanned, waterproofed, and cleaned 3D meshes for 150 broken
objects, paired and geometrically aligned with complete counterparts. Fantastic
Breaks contains class and material labels, proxy repair parts that join to
broken meshes to generate complete meshes, and manually annotated fracture
boundaries. Through a detailed analysis of fracture geometry, we reveal
differences between Fantastic Breaks and synthetic fracture datasets generated
using geometric and physics-based methods. We show experimental shape repair
evaluation with Fantastic Breaks using multiple learning-based approaches
pre-trained with synthetic datasets and re-trained with subset of Fantastic
Breaks.
