Multi-directional 3D printing has the capability of decreasing or eliminating the need for support structures. Recent work proposed a beam-guided search algorithm to find an optimized sequence of plane-clipping, which gives volume decomposition of a given 3D model. Different printing directions are employed in different regions to fabricate a model with tremendously less support (or even no support in many cases).To obtain optimized decomposition, a large beam width needs to be used in the search algorithm, leading to a very time-consuming computation. In this paper, we propose a learning framework that can accelerate the beam-guided search by using a smaller number of the original beam width to obtain results with similar quality. Specifically, we use the results of beam-guided search with large beam width to train a scoring function for candidate clipping planes based on six newly proposed feature metrics. With the help of these feature metrics, both the current and the sequence-dependent information are captured by the neural network to score candidates of clipping. As a result, we can achieve around 3x computational speed. We test and demonstrate our accelerated decomposition on a large dataset of models for 3D printing.

中文翻译：

---

学习加速多向 3D 打印的分解

多向 3D 打印能够减少或消除对支撑结构的需求。最近的工作提出了一种波束引导搜索算法，以找到优化的平面裁剪序列，从而对给定的 3D 模型进行体积分解。在不同的区域采用不同的打印方向来制造一个支持极少（甚至在许多情况下没有支持）的模型。 为了获得优化的分解，搜索算法需要使用大束宽，导致非常耗时 -消耗计算。在本文中，我们提出了一种学习框架，该框架可以通过使用较少数量的原始波束宽度来加速波束引导搜索，以获得具有相似质量的结果。具体来说，我们使用大波束宽度的波束引导搜索的结果来训练基于六个新提出的特征度量的候选裁剪平面的评分函数。在这些特征度量的帮助下，当前信息和序列相关信息都被神经网络捕获，以对剪辑的候选者进行评分。因此，我们可以实现大约 3 倍的计算速度。我们在用于 3D 打印的大型模型数据集上测试并演示了我们的加速分解。