We present a generative model to synthesize 3D shapes as sets of handles -- lightweight proxies that approximate the original 3D shape -- for applications in interactive editing, shape parsing, and building compact 3D representations. Our model can generate handle sets with varying cardinality and different types of handles (Figure 1). Key to our approach is a deep architecture that predicts both the parameters and existence of shape handles, and a novel similarity measure that can easily accommodate different types of handles, such as cuboids or sphere-meshes. We leverage the recent advances in semantic 3D annotation as well as automatic shape summarizing techniques to supervise our approach. We show that the resulting shape representations are intuitive and achieve superior quality than previous state-of-the-art. Finally, we demonstrate how our method can be used in applications such as interactive shape editing, completion, and interpolation, leveraging the latent space learned by our model to guide these tasks. Project page: http://mgadelha.me/shapehandles.

该研究提出了一种生成模型来合成3D形状，以句柄集的形式——轻量级代理，为交互式编辑、形状解析和构建紧凑的3D表示提供应用，通过监督自动形状概括技术，展示了直观的形状表示，并通过我们的模型学习的潜在空间来指导交互形状编辑，补充和插值等任务，实现了卓越的质量。

学习形状手柄的生成模型