Dynamic Guided Network for Monocular Depth Estimation

Self-attention and encoder-decoder have been widely used in the deep neural network for monocular depth estimation. The self-attention mechanism is capable of capturing long-range dependencies by computing the representation of each image position by a weighted sum of the features at all positions, while the encoder-decoder can capture detailed structural information by gradually recovering spatial information. In this work, we combine the advantages of both methods. Specifically, our proposed model, DGNet, extends EMANet [1] by adding an effective decoder module to progressively refine the coarse depth map. In the decoder stage, we design a dynamic guided upsampling module that employs dynamically generated kernel conditioned on low-level features to guide the upsampling of the coarse depth map. Experimental results demonstrate that our method obtains higher accuracy and generates visually pleasant depth maps.