  This paper presents an unsupervised deep learning framework called UnDEMoN
for estimating dense depth map and 6-DoF camera pose information directly from
monocular images. The proposed network is trained using unlabeled monocular
stereo image pairs and is shown to provide superior performance in depth and
ego-motion estimation compared to the existing state-of-the-art. These
improvements are achieved by introducing a new objective function that aims to
minimize spatial as well as temporal reconstruction losses simultaneously.
These losses are defined using bi-linear sampling kernel and penalized using
the Charbonnier penalty function. The objective function, thus created,
provides robustness to image gradient noises thereby improving the overall
estimation accuracy without resorting to any coarse to fine strategies which
are currently prevalent in the literature. Another novelty lies in the fact
that we combine a disparity-based depth estimation network with a pose
estimation network to obtain absolute scale-aware 6 DOF Camera pose and
superior depth map. The effectiveness of the proposed approach is demonstrated
through performance comparison with the existing supervised and unsupervised
methods on the KITTI driving dataset.
