  In this paper, we propose a novel method for the challenging problem of
guided depth map super-resolution, called PAGNet. It is based on residual dense
networks and involves the attention mechanism to suppress the texture copying
problem arises due to improper guidance by RGB images. The attention module
mainly involves providing the spatial attention to guidance image based on the
depth features. We evaluate the proposed trained models on test dataset and
provide comparisons with the state-of-the-art depth super-resolution methods.
