A multiscale dilated convolution and mixed-order attention-based deep neural network for monocular depth prediction

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Recovering precise depth information from different scenes has become a popular subject in the semantic segmentation and virtual reality fields. This study presents a multiscale dilated convolution and mixed-order attention-based deep neural network for monocular depth recovery. Specifically, we design a multilevel feature enhancement scheme to enhance and fuse high-resolution and low-resolution features on the basis of mixed-order attention. Moreover, a multiscale dilated convolution module that combines four different dilated convolutions is explored for deriving multiscale information and increasing the receptive field. Recent studies have shown that the design of loss terms is crucial to depth prediction. Therefore, an efficient loss function that combines the ℓ1 loss, gradient loss, and classification loss is also designed to promote rich details. Experiments on three public datasets show that the presented approach achieves better performance than state-of-the-art depth prediction methods.