Monocular Depth Recovery Based on Self-Attention Mechanism and Transfer Learning

The task of depth recovery is to calculate the depth map using only the rgb images. The existing deep network greatly compresses the resolution in the process of feature extraction, resulting in serious loss of depth information in the edge area of the object with large depth changes and the area far away from the sensor. This paper proposes a convolutional neural network based on self-attention mechanism, which can use a single rgb image to calculate its corresponding high-resolution depth map. Specifically, we use the Fully Convolutional Network with jump connections and bilinear upsampling, and add a self-attention mechanism module inside it. The self-attention mechanism module aggregates the features at each location through the weighting and selective aggregation of the features at all locations. Compared with the baseline, our method has more obvious object edge information, and also has better results on some indicators.