论文标题
Breaking Bad：用于几何断裂和重新组装的数据集
Breaking Bad: A Dataset for Geometric Fracture and Reassembly
论文作者
论文摘要
我们介绍了Breaking Bad，这是一个大规模的破裂对象数据集。我们的数据集由超过一百万个从一万个基本型号模拟的破裂对象组成。断裂模拟由最近基于物理的算法提供动力，该算法有效地生成了对象的各种断裂模式。现有的形状组件数据集根据语义上有意义的部分分解对象，从而有效地对构建过程进行建模。相比之下，打破糟糕的模型几何对象如何自然变成碎片的破坏过程。我们的数据集是一个基准，可以研究破裂的对象重新组装，并为几何形状理解带来了新的挑战。我们通过几种几何测量值分析了数据集，并在各种设置下基准了三种最先进的组装深度学习方法。广泛的实验结果表明了我们的数据集的困难，呼吁专门针对几何形状组装任务的模型设计中的未来研究。我们在https://breaking-bad-dataset.github.io/上托管数据集。
We introduce Breaking Bad, a large-scale dataset of fractured objects. Our dataset consists of over one million fractured objects simulated from ten thousand base models. The fracture simulation is powered by a recent physically based algorithm that efficiently generates a variety of fracture modes of an object. Existing shape assembly datasets decompose objects according to semantically meaningful parts, effectively modeling the construction process. In contrast, Breaking Bad models the destruction process of how a geometric object naturally breaks into fragments. Our dataset serves as a benchmark that enables the study of fractured object reassembly and presents new challenges for geometric shape understanding. We analyze our dataset with several geometry measurements and benchmark three state-of-the-art shape assembly deep learning methods under various settings. Extensive experimental results demonstrate the difficulty of our dataset, calling on future research in model designs specifically for the geometric shape assembly task. We host our dataset at https://breaking-bad-dataset.github.io/.
