PAG-Net: Progressive Attention Guided Depth Super-resolution Network
This addresses depth super-resolution for applications like robotics or AR/VR, but appears incremental as it builds on existing networks with attention.
The paper tackles the problem of guided depth map super-resolution by introducing PAGNet, which uses an attention mechanism to reduce texture copying from RGB guidance, and reports comparisons with state-of-the-art methods.
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.