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Automated shape repair approaches currently lack access to datasets thatdescribe real-world damaged geometry. We present Fantastic Breaks (and Where toFind Them:https://terascale-all-sensing-research-studio.github.io/FantasticBreaks), adataset containing scanned, waterproofed, and cleaned 3D meshes for 150 brokenobjects, paired and geometrically aligned with complete counterparts. FantasticBreaks contains class and material labels, proxy repair parts that join tobroken meshes to generate complete meshes, and manually annotated fractureboundaries. Through a detailed analysis of fracture geometry, we revealdifferences between Fantastic Breaks and synthetic fracture datasets generatedusing geometric and physics-based methods. We show experimental shape repairevaluation with Fantastic Breaks using multiple learning-based approachespre-trained with synthetic datasets and re-trained with subset of FantasticBreaks.

Fantastic Breaks: A Dataset of Paired 3D Scans of Real-World Broken Objects and Their Complete Counterparts