Self-supervised Monocular Depth Estimation for Dynamic Objects with Ground Propagation

Self-supervised single-view depth estimation, trained on video sequences, faces significant challenges when dynamic objects are present in the training data, as they violate the basic multi-view geometry assumptions used to compute photometric losses. We propose a novel approach that leverages the relationship between the depth of moving objects and their ground contact points. By iteratively propagating ground features to moving targets in perceptual layers, we recalibrate the depth of dynamic entities while preserving details. Our method maintains the end-to-end training paradigm without additional networks or complex training procedures. Our experiments demonstrate that our method achieves state-of-the-art performance when estimating depth for dynamic objects and attains superior generalization compared to existing approaches. The relevant experimental code can be accessed at: https://github.com/LiHuanLi/GroundMono