  Recent advances in end-to-end unsupervised learning has significantly
improved the performance of monocular depth prediction and alleviated the
requirement of ground truth depth. Although a plethora of work has been done in
enforcing various structural constraints by incorporating multiple losses
utilising smoothness, left-right consistency, regularisation and matching
surface normals, a few of them take into consideration multi-scale structures
present in real world images. Most works utilise a VGG16 or ResNet50 model
pre-trained on ImageNet weights for predicting depth. We propose a deep feature
fusion method utilising features at multiple scales for learning
self-supervised depth from scratch. Our fusion network selects features from
both upper and lower levels at every level in the encoder network, thereby
creating multiple feature pyramid sub-networks that are fed to the decoder
after applying the CoordConv solution. We also propose a refinement module
learning higher scale residual depth from a combination of higher level deep
features and lower level residual depth using a pixel shuffling framework that
super-resolves lower level residual depth. We select the KITTI dataset for
evaluation and show that our proposed architecture can produce better or
comparable results in depth prediction.
