  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/.
