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"幻妙裂隙"（及寻踪之法：https://terascale-all-sensing-research-studio.github.io/FantasticBreaks），该数据集包含150个破损物体的经扫描、防水化处理及清洁的三维网格，每个样本均与对应完整物体配对并实现几何对齐。Fantastic Breaks包含类别与材质标签、可与破损网格拼接以生成完整网格的代理修复部件，以及人工标注的断裂边界。通过对断裂几何的详细分析，我们揭示了Fantastic Breaks与基于几何及物理方法生成的合成断裂数据集之间的差异。我们展示了基于Fantastic Breaks的实验性形状修复评估，该评估采用多种基于学习的方法，使用合成数据集进行预训练，并利用Fantastic Breaks子集进行重新训练。