  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.
