Supplementary Material Canonical Surface Mapping via Geometric Cycle Consistency
We use a 5 Layer UNet [4] with 4 x4 convolution at each layer. The UNet takes input an image and learns to predict an unit vector which parameterizes u, v. Along with that we also train the UNet to predict a segmentation of the object which is necessary for keypoint evaluations. We train our networks for over 200 epochs on all the datasets independently. We use Adam [2] for optimization of our neural network with a learning rate of 10 4.