  Deep models are capable of fitting complex high dimensional functions while
usually yielding large computation load. There is no way to speed up the
inference process by classical lookup tables due to the high-dimensional input
and limited memory size. Recently, a novel architecture (PointNet) for point
clouds has demonstrated that it is possible to obtain a complicated deep
function from a set of 3-variable functions. In this paper, we exploit this
property and apply a lookup table to encode these 3-variable functions. This
method ensures that the inference time is only determined by the memory access
no matter how complicated the deep function is. We conduct extensive
experiments on ModelNet and ShapeNet datasets and demonstrate that we can
complete the inference process in 1.5 ms on an Intel i7-8700 CPU (single core
mode), 32x speedup over the PointNet architecture without any performance
degradation.

Computer Vision and Pattern Recognition;Computer Science and Game Theory;Machine Learning