  We propose a novel objective for penalizing geometric inconsistencies to
improve the depth and pose estimation performance of monocular camera images.
Our objective is designed using the Wasserstein distance between two point
clouds, estimated from images with different camera poses. The Wasserstein
distance can impose a soft and symmetric coupling between two point clouds,
which suitably maintains geometric constraints and results in a differentiable
objective. By adding our objective to the those of other state-of-the-art
methods, we can effectively penalize geometric inconsistencies and obtain
highly accurate depth and pose estimations. Our proposed method is evaluated
using the KITTI dataset.
