Structure Generation and Guidance Network for Unsupervised Monocular Depth Estimation

Structure information is important to unsupervised depth learning from monocular videos. However, most existing methods focus on depth smoothing on planar regions, while other structure information, such as object shape and surface curvature, is ignored. In this work, we propose SGGN, a novel Structure Generation and Guidance Network to refine depth estimation under the guidance of extracted image structure. We introduce second-order Domain Transform filtering, which explores spatial depth variation by gradient propagation, to capture long-range dependence in the extracted structure for depth refinement. Then, several structure-aware constraints, as well as an attention mechanism, are applied to guide the training of SGGN, which leads to better depth estimation with structural guidance. Notably, our structure-aware constraints are designed in terms of different characteristics. Experiments on three benchmarks demonstrate the effectiveness of our structure-guided model and its state-of-the-art performance for unsupervised depth estimation.