  Monocular depth estimation is an important task that can be applied to many
robotic applications. Existing methods focus on improving depth estimation
accuracy via training increasingly deeper and wider networks, however these
suffer from large computational complexity. Recent studies found that edge
information are important cues for convolutional neural networks (CNNs) to
estimate depth. Inspired by the above observations, we present a novel
lightweight Edge Guided Depth Estimation Network (EGD-Net) in this study. In
particular, we start out with a lightweight encoder-decoder architecture and
embed an edge guidance branch which takes as input image gradients and
multi-scale feature maps from the backbone to learn the edge attention
features. In order to aggregate the context information and edge attention
features, we design a transformer-based feature aggregation module (TRFA). TRFA
captures the long-range dependencies between the context information and edge
attention features through cross-attention mechanism. We perform extensive
experiments on the NYU depth v2 dataset. Experimental results show that the
proposed method runs about 96 fps on a Nvidia GTX 1080 GPU whilst achieving the
state-of-the-art performance in terms of accuracy.
