The operations comprising the T-Net are motivated by the higher-level architecture of PointNet. MLPs (or fully-connected layers) are used to map the input points independently and identically to a higher-dimensional space; max-pooling is used to encode a global feature vector whose dimensionality is then reduced with fully-connected layers. The input-dependent features at the final fully-connected layer are then combined with globally trainable weights and biases, resulting in a 3-by-3 transformation matrix.