Deep hierarchical guidance and regularization learning for end-to-end depth estimation

In this work, we propose a novel deep Hierarchical Guidance and Regularization (HGR) learning framework for end-to-end monocular depth estimation, which well integrates a hierarchical depth guidance network and a hierarchical regularization learning method for fine-grained depth prediction. The two properties in our proposed HGR framework can be summarized as: (1) the hierarchical depth guidance network automatically learns hierarchical depth representations by supervision guidance and multiple side conv-operations from the basic CNN, leveraging the learned hierarchical depth representations to progressively guide the upsampling and prediction process of upper deconv-layers; (2) the hierarchical regularization learning method integrates various-level information of depth maps, optimizing the network to predict depth maps with similar structure to ground truth. Comprehensive evaluations over three public benchmark datasets (including NYU Depth V2, KITTI and Make3D datasets) well demonstrate the state-of-the-art performance of our proposed depth estimation framework.