Learning to Accelerate Decomposition for Multi-Directional 3D Printing

As a strong complementary of additive manufacturing, 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 so that a model can be fabricated with tremendously less supports (or even no support in many cases). To obtain optimized decomposition, a large beam width needs to be used in the search algorithm, which therefore leads to a very time-consuming computation. In this paper, we propose a learning framework that can accelerate the beam-guided search by using only 1/2 of the original beam width to obtain results with similar quality. Specifically, we train a classifier for each pair of candidate clipping planes based on six newly proposed feature metrics from the results of beam-guided search with large beam width. With the help of these feature metrics, both the current and the sequence-dependent information are captured by the classifier to score candidates of clipping. As a result, we can achieve around 2 times acceleration. We test and demonstrate the performance of our accelerated decomposition on a large dataset of models for 3D printing.