Leveraging Contextual Information for Monocular Depth Estimation

Humans strongly rely on visual cues to understand scenes such as segmenting, detecting objects, or measuring the distance from nearby objects. Recent studies suggest that deep neural networks can take advantage of contextual representation for the estimation of a depth map for a given image. Therefore, focusing on the scene context can be beneficial for successful depth estimation. In this study, a novel network architecture is proposed to improve the performance by leveraging the contextual information for monocular depth estimation. We introduce a depth prediction network with the proposed attentive skip connection and a global context module, to obtain meaningful semantic features and enhance the performance of the model. Furthermore, our model is validated through several experiments on the KITTI and NYU Depth V2 datasets. The experimental results demonstrate the effectiveness of the proposed network, which achieves a state-of-the-art monocular depth estimation performance while maintaining a high running speed.