Self-Supervised Depth and Pose Estimation Through Iterative Epipolar Sampling and Refinement Towards Equilibrium

The authors propose a self-supervised depth and pose estimation model, DualRefine, that tightly couples depth and pose estimation through a feedback loop. The model iteratively refines depth estimates and a hidden state of feature maps by computing local matching costs based on epipolar geometry, and uses the refined depth estimates and feature maps to compute pose updates at each step.