  Self-supervised multi-frame depth estimation achieves high accuracy by
computing matching costs of pixel correspondences between adjacent frames,
injecting geometric information into the network. These pixel-correspondence
candidates are computed based on the relative pose estimates between the
frames. Accurate pose predictions are essential for precise matching cost
computation as they influence the epipolar geometry. Furthermore, improved
depth estimates can, in turn, be used to align pose estimates.
  Inspired by traditional structure-from-motion (SfM) principles, we propose
the DualRefine model, which tightly couples depth and pose estimation through a
feedback loop. Our novel update pipeline uses a deep equilibrium model
framework to iteratively refine depth estimates and a hidden state of feature
maps by computing local matching costs based on epipolar geometry. Importantly,
we used the refined depth estimates and feature maps to compute pose updates at
each step. This update in the pose estimates slowly alters the epipolar
geometry during the refinement process. Experimental results on the KITTI
dataset demonstrate competitive depth prediction and odometry prediction
performance surpassing published self-supervised baselines.
