PAG-Net: Progressive Attention Guided Depth Super-resolution Network

TL;DR

PAG-Net introduces a progressive attention-guided approach for depth map super-resolution, effectively suppressing texture copying by leveraging spatial attention mechanisms to improve guidance from RGB images.

Contribution

It presents a novel deep network architecture that integrates residual dense blocks with attention modules for enhanced depth super-resolution.

Findings

Outperforms state-of-the-art methods on test datasets.

Effectively suppresses texture copying artifacts.

Demonstrates improved depth map quality with attention mechanisms.

Abstract

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.