  We consider the problem of unsupervised camera pose estimation. Given an
input video sequence, our goal is to estimate the camera pose (i.e. the camera
motion) between consecutive frames. Traditionally, this problem is tackled by
placing strict constraints on the transformation vector or by incorporating
optical flow through a complex pipeline. We propose an alternative approach
that utilizes a compositional re-estimation process for camera pose estimation.
Given an input, we first estimate a depth map. Our method then iteratively
estimates the camera motion based on the estimated depth map. Our approach
significantly improves the predicted camera motion both quantitatively and
visually. Furthermore, the re-estimation resolves the problem of
out-of-boundaries pixels in a novel and simple way. Another advantage of our
approach is that it is adaptable to other camera pose estimation approaches.
Experimental analysis on KITTI benchmark dataset demonstrates that our method
outperforms existing state-of-the-art approaches in unsupervised camera
ego-motion estimation.
