  In this paper, we provide an improved version of UnDEMoN model for depth and
ego motion estimation from monocular images. The improvement is achieved by
combining the standard bi-linear sampler with a deep network based image
sampling model (DIS-NET) to provide better image reconstruction capabilities on
which the depth estimation accuracy depends in un-supervised learning models.
While DIS-NET provides higher order regression and larger input search space,
the bi-linear sampler provides geometric constraints necessary for reducing the
size of the solution space for an ill-posed problem of this kind. This
combination is shown to provide significant improvement in depth and pose
estimation accuracy outperforming all existing state-of-the-art methods in this
category. In addition, the modified network uses far less number of tunable
parameters making it one of the lightest deep network model for depth
estimation. The proposed model is labeled as "UnDEMoN 2.0" indicating an
improvement over the existing UnDEMoN model. The efficacy of the proposed model
is demonstrated through rigorous experimental analysis on the standard KITTI
dataset.

Computer Vision and Pattern Recognition