Segmentation outputs should be unchanged if the object undergoes certain transformations, such as translation or scaling. For a given input point cloud, we apply an appropriate rigid or affine transformation to achieve pose normalization. Because each of the n input points are represented as a vector and are mapped to the embedding spaces independently, applying a geometric transformation simply amounts to matrix multiplying each point with a transformation matrix. This is motivated by the concept of Spatial Transformer Networks.