This can also be used for object recognition against invariant 3D representations, potentially with more accuracy than traditional convolutional neural net arch

This can also be used for object recognition against invariant 3D representations, potentially with more accuracy than traditional convolutional neural net architectures. Consider: their proof-of-concept face-recognition model achieves performance comparable to traditional convnets on faces with varying degree of pose, lighting, shape and texture, even though it was trained completely unsupervised. I would expect this type of model to beat the state of the art in face recognition and other similar tasks when fined-tuned with supervised training in the not-too-distant future.