Zengping Chen

HI-Net: Boosting Self-Supervised Indoor Depth Estimation via Pose Optimization

Pose estimation plays a critical role in self-supervised monocular depth estimation for indoor scenes, especially those involving complex ego-motion. In this letter, we leverage the two-view geometry constraints into pose estimation to boost the accuracy of pose estimation, which ultimately improves