Given the unstructured nature of point cloud data, a scan made up of n points has n! permutations. The subsequent data processing must be invariant to the different representations. In order to make PointNet invariant to input permutations, we use a symmetric function (such as max-pooling) once the n input points are mapped to higher-dimensional space. The result is a global feature vector that aims to capture an aggregate signature of the n input points. The global feature vector is used alongside local point features for segmentation.