Abstracting human-made 3D models by a set of primitives, such as cuboid abstraction, is a fundamental task in 3D shape modelling and analysis. Traditionally, different forms of representations, such as edges, volumes, or curves, were used as primitives. Although methods that apply local operations to compute such primitives can produce satisfactory results with their own merits, the computations can be very slow with complex models. Learning-based abstraction methods are much faster but cannot guarantee the fitting precision between the primitives and the original shape. To solve this problem, we propose an unsupervised learning approach for shape abstraction. Our method’s key idea is to use an iterative error feedback (IEF)-based network to improve primitive precision. Our method contains two main steps. First, we use a regression network to predict the initial primitives. Second, we increase the accuracy of the initial primitives by using an IEF-based network, which iteratively outputs the primitive updates. We demonstrate the advantages of our method by comparing it to existing state-of-the-art methods. We also thoroughly evaluate our method by ablation studies.

通过一组基元（例如长方体抽象）抽象人造 3D 模型是 3D 形状建模和分析中的一项基本任务。传统上，不同形式的表示，例如边、体积或曲线，被用作图元。尽管应用本地操作来计算此类原语的方法可以凭借其自身的优点产生令人满意的结果，但对于复杂模型，计算可能会非常缓慢。基于学习的抽象方法要快得多，但不能保证图元与原始形状之间的拟合精度。为了解决这个问题，我们提出了一种用于形状抽象的无监督学习方法。我们方法的关键思想是使用基于迭代错误反馈 (IEF) 的网络来提高原始精度。我们的方法包含两个主要步骤。第一的，我们使用回归网络来预测初始基元。其次，我们通过使用基于 IEF 的网络来提高初始原语的准确性，该网络迭代地输出原语更新。我们通过将其与现有的最先进方法进行比较来证明我们的方法的优势。我们还通过消融研究彻底评估了我们的方法。