![]() ![]() It thus enables efficient geometry processing algorithms specifically designed for convex shapes and has been widely used in game engines, physics simulations, and animation. Īpproximate convex decomposition aims to decompose a 3D shape into a set of almost convex components, whose convex hulls can then be used to represent the input shape. Extensive experimental results demonstrate the difficulty of our dataset, calling on future research in model designs specifically for the geometric shape assembly task. We analyze our dataset with several geometry measurements and benchmark three state-of-the-art shape assembly deep learning methods under various settings. Our dataset serves as a benchmark that enables the study of fractured object reassembly and presents new challenges for geometric shape understanding. In contrast, Breaking Bad models the destruction process of how a geometric object naturally breaks into fragments. ![]() ![]() Existing shape assembly datasets decompose objects according to semantically meaningful parts, effectively modeling the construction process. The fracture simulation is powered by a recent physically based algorithm that efficiently generates a variety of fracture modes of an object. Our dataset consists of over one million fractured objects simulated from ten thousand base models. We introduce Breaking Bad, a large-scale dataset of fractured objects. ![]()
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