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.

中文翻译：

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学习形状句柄的生成模型

我们提出了一个生成模型，将 3D 形状合成为一组句柄——近似原始 3D 形状的轻量级代理——用于交互式编辑、形状解析和构建紧凑 3D 表示的应用程序。我们的模型可以生成具有不同基数和不同类型句柄的句柄集（图 1）。我们方法的关键是预测形状手柄的参数和存在的深层架构，以及可以轻松适应不同类型手柄（例如长方体或球体网格）的新颖相似性度量。我们利用语义 3D 注释和自动形状总结技术的最新进展来监督我们的方法。我们表明，由此产生的形状表示是直观的，并且比以前的最先进技术实现了更高的质量。最后，我们展示了我们的方法如何用于交互式形状编辑、完成和插值等应用程序，利用我们的模型学习的潜在空间来指导这些任务。项目页面：http://mgadelha.me/shapehandles。