  A significant weakness of most current deep Convolutional Neural Networks is
the need to train them using vast amounts of manu- ally labelled data. In this
work we propose a unsupervised framework to learn a deep convolutional neural
network for single view depth predic- tion, without requiring a pre-training
stage or annotated ground truth depths. We achieve this by training the network
in a manner analogous to an autoencoder. At training time we consider a pair of
images, source and target, with small, known camera motion between the two such
as a stereo pair. We train the convolutional encoder for the task of predicting
the depth map for the source image. To do so, we explicitly generate an inverse
warp of the target image using the predicted depth and known inter-view
displacement, to reconstruct the source image; the photomet- ric error in the
reconstruction is the reconstruction loss for the encoder. The acquisition of
this training data is considerably simpler than for equivalent systems,
requiring no manual annotation, nor calibration of depth sensor to camera. We
show that our network trained on less than half of the KITTI dataset (without
any further augmentation) gives com- parable performance to that of the state
of art supervised methods for single view depth estimation.
