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.

本文提出了一种用于自监督多帧深度估计的DualRefine模型，通过反馈循环紧密耦合深度和姿态估计，使用一个深度均衡模型框架来迭代地改进深度估计，同时结合极线几何计算局部匹配成本，证明在KITTI数据集上具有竞争性的深度预测和里程计预测表现，超过了已有的自监督方法。

DualRefine: 应用自监督方法的深度及姿态估计技术，通过迭代次数的外极线采样及精度提高达到平衡状态