PointNet consists of two core components. The primary MLP network, and the transformer net (T-net). The T-net aims to learn an affine transformation matrix by its own mini network. The T-net is used twice. The first time to transform the input features (n, 3) into a canonical representation. The second is an affine transformation for alignment in feature space (n, 3). As per the original paper we constrain the transformation to be close to an orthogonal matrix (i.e. ||X*X^T - I|| = 0).