Rotation-Invariant Local-to-Global Representation Learning for 3D Point Cloud Supplementary Document

Our model is implemented in Tensorflow. For the 3D object classification experiments, the learning rate is 0.001 and the batch size is 32. In each hierarchy, the number of neighbors for the stochastic dilated k-NNs, denoted by k, is sampled from the following three different uniform distributions: U(32, 96), U(16, 48), and U(8, 24). The dilation rate employed in the first descriptor extraction stage is sampled from U(2, 4). Meanwhile, the number of edges in the graph, ˆk, is given by sampling from U(8, 24), U(4, 12), and U(2, 8).