Dual Attention Guidance Network for Self-Supervised Monocular Depth Estimation

Self-supervised monocular depth estimation shows great promise since only a single camera is required. However, most existing methods fail to model the geometric structure of objects, leading to poor performance in object boundary depth estimation. To overcome these shortcomings, a dual attention guidance network (DAG-Net), containing two complementary modules termed depth-guided attention module (DAM) and semantic-guided multi-modal attention module (SAM), is proposed in this paper. The DAM utilizes depth features to guide semantic features through multi-head attention. When semantic features are well learned, they guide depth features to learn useful geometric representations through backpropagation. Besides, the SAM is proposed to incorporate multi-modal data from depth estimation and semantic segmentation predictions at different scales. To eliminate the mutual interference between DAM and SAM, we also propose a two-stage training strategy to adjust the convergence direction during the training process. The effectiveness of our proposed DAG-Net is qualitatively and quantitatively verified by various experiments on KITTI, Cityscapes, and Make3D datasets, showing outstanding performance compared with the state-of-the-art methods.