  We propose DFPNet -- an unsupervised, joint learning system for monocular
Depth, Optical Flow and egomotion (Camera Pose) estimation from monocular image
sequences. Due to the nature of 3D scene geometry these three components are
coupled. We leverage this fact to jointly train all the three components in an
end-to-end manner. A single composite loss function -- which involves image
reconstruction-based loss for depth & optical flow, bidirectional consistency
checks and smoothness loss components -- is used to train the network. Using
hyperparameter tuning, we are able to reduce the model size to less than 5%
(8.4M parameters) of state-of-the-art DFP models. Evaluation on KITTI and
Cityscapes driving datasets reveals that our model achieves results comparable
to state-of-the-art in all of the three tasks, even with the significantly
smaller model size.
