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.

翻译：自动化形状修复方法目前缺乏描述真实世界损伤几何结构的数据集。我们提出"神奇断裂（及其发现之道：https://terascale-all-sensing-research-studio.github.io/FantasticBreaks）"数据集，包含150个破损物体的经扫描、防水处理和清洁的三维网格，这些网格与完整对应物实现配对与几何对齐。该数据集包含类别与材料标签、可拼接至破损网格以生成完整网格的替代修复部件，以及人工标注的断裂边界。通过对断裂几何结构的详细分析，我们揭示了"神奇断裂"与通过几何和物理方法生成的合成断裂数据集之间的差异。我们展示了使用"神奇断裂"的实验性形状修复评估，基于多种预训练于合成数据集并利用"神奇断裂"子集重新训练的深度学习方法。