  Recent work has made significant progress in learning object meshes with weak
supervision. Soft Rasterization methods have achieved accurate 3D
reconstruction from 2D images with viewpoint supervision only. In this work, we
further reduce the labeling effort by allowing such 3D reconstruction methods
leverage unlabeled images. In order to obtain the viewpoints for these
unlabeled images, we propose to use a Siamese network that takes two images as
input and outputs whether they correspond to the same viewpoint. During
training, we minimize the cross entropy loss to maximize the probability of
predicting whether a pair of images belong to the same viewpoint or not. To get
the viewpoint of a new image, we compare it against different viewpoints
obtained from the training samples and select the viewpoint with the highest
matching probability. We finally label the unlabeled images with the most
confident predicted viewpoint and train a deep network that has a
differentiable rasterization layer. Our experiments show that even labeling
only two objects yields significant improvement in IoU for ShapeNet when
leveraging unlabeled examples. Code is available at
https://github.com/IssamLaradji/SSR.
