  Self-supervised learning of depth has been a highly studied topic of research
as it alleviates the requirement of having ground truth annotations for
predicting depth. Depth is learnt as an intermediate solution to the task of
view synthesis, utilising warped photometric consistency. Although it gives
good results when trained using stereo data, the predicted depth is still
sensitive to noise, illumination changes and specular reflections. Also,
occlusion can be tackled better by learning depth from a single camera. We
propose ADAA, utilising depth augmentation as depth supervision for learning
accurate and robust depth. We propose a relational self-attention module that
learns rich contextual features and further enhances depth results. We also
optimize the auto-masking strategy across all losses by enforcing L1
regularisation over mask. Our novel progressive training strategy first learns
depth at a lower resolution and then progresses to the original resolution with
slight training. We utilise a ResNet18 encoder, learning features for
prediction of both depth and pose. We evaluate our predicted depth on the
standard KITTI driving dataset and achieve state-of-the-art results for
monocular depth estimation whilst having significantly lower number of
trainable parameters in our deep learning framework. We also evaluate our model
on Make3D dataset showing better generalization than other methods.
